Digex Qualtrics Survey Responses

Dataset statistics

Number of variables	50
Number of observations	499
Missing cells	6257
Missing cells (%)	25.1%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	134.4 KiB
Average record size in memory	275.8 B

Variable types

Numeric	14
Categorical	33
Unsupported	3

Alerts

`ethic_appr` has a high cardinality: 498 distinct values	High cardinality
`study_1_conc` has a high cardinality: 253 distinct values	High cardinality
`study_1_add_info` has a high cardinality: 90 distinct values	High cardinality
`study_2_conc` has a high cardinality: 274 distinct values	High cardinality
`study_2_add_info` has a high cardinality: 91 distinct values	High cardinality
`study_3_conc` has a high cardinality: 227 distinct values	High cardinality
`study_3_add_info` has a high cardinality: 76 distinct values	High cardinality
`study_4_conc` has a high cardinality: 253 distinct values	High cardinality
`study_4_add_info` has a high cardinality: 87 distinct values	High cardinality
`design_add_fac` has a high cardinality: 192 distinct values	High cardinality
`rank_add_fac_1` has a high cardinality: 67 distinct values	High cardinality
`df_index` is highly correlated with `study_1_add_info` and 4 other fields	High correlation
`age` is highly correlated with `study_1_add_info` and 5 other fields	High correlation
`rank_sci_repro` is highly correlated with `study_1_add_info` and 6 other fields	High correlation
`rank_resp` is highly correlated with `study_1_add_info` and 6 other fields	High correlation
`rank_just` is highly correlated with `study_1_add_info` and 6 other fields	High correlation
`rank_anony` is highly correlated with `study_1_add_info` and 6 other fields	High correlation
`rank_harms` is highly correlated with `study_1_add_info` and 5 other fields	High correlation
`rank_balance` is highly correlated with `study_1_add_info` and 8 other fields	High correlation
`rank_pub_interst` is highly correlated with `study_1_add_info` and 3 other fields	High correlation
`rank_add_fac_1_pos` is highly correlated with `study_1_add_info` and 7 other fields	High correlation
`rank_add_fac_2_pos` is highly correlated with `politic_views` and 8 other fields	High correlation
`rank_add_fac_3_pos` is highly correlated with `study_1_add_info` and 7 other fields	High correlation
`aware_sm_advan_score` is highly correlated with `study_1_add_info` and 6 other fields	High correlation
`aware_sm_use_score` is highly correlated with `study_1_add_info` and 2 other fields	High correlation
`sm_use` is highly correlated with `study_1_add_info` and 5 other fields	High correlation
`gender_id` is highly correlated with `ethnic_id` and 1 other fields	High correlation
`ethnic_id` is highly correlated with `gender_id` and 6 other fields	High correlation
`edu` is highly correlated with `study_1_add_info` and 4 other fields	High correlation
`politic_views` is highly correlated with `ethnic_id` and 5 other fields	High correlation
`aware_sm_res` is highly correlated with `study_1_add_info` and 4 other fields	High correlation
`study_1_ethic_acc` is highly correlated with `study_1_add_info` and 8 other fields	High correlation
`study_1_add_info` is highly correlated with `df_index` and 35 other fields	High correlation
`study_2_ethic_acc` is highly correlated with `study_1_ethic_acc` and 8 other fields	High correlation
`study_2_add_info` is highly correlated with `df_index` and 28 other fields	High correlation
`study_3_ethic_acc` is highly correlated with `study_1_add_info` and 6 other fields	High correlation
`study_3_add_info` is highly correlated with `df_index` and 36 other fields	High correlation
`study_4_ethic_acc` is highly correlated with `study_1_ethic_acc` and 8 other fields	High correlation
`study_4_add_info` is highly correlated with `df_index` and 38 other fields	High correlation
`design_cont` is highly correlated with `study_1_add_info` and 13 other fields	High correlation
`design_num_users` is highly correlated with `study_1_add_info` and 12 other fields	High correlation
`design_res_purp` is highly correlated with `study_1_add_info` and 11 other fields	High correlation
`design_len_data` is highly correlated with `study_2_add_info` and 11 other fields	High correlation
`design_admin_inter` is highly correlated with `study_2_add_info` and 11 other fields	High correlation
`design_inter_type` is highly correlated with `study_1_add_info` and 9 other fields	High correlation
`design_partic_aware` is highly correlated with `study_1_add_info` and 6 other fields	High correlation
`design_inter_impact` is highly correlated with `study_1_add_info` and 12 other fields	High correlation
`design_type_data` is highly correlated with `study_1_add_info` and 10 other fields	High correlation
`rank_add_fac_1` is highly correlated with `sm_use` and 27 other fields	High correlation
`rank_add_fac_2` is highly correlated with `sm_use` and 27 other fields	High correlation
`rank_add_fac_3` is highly correlated with `df_index` and 29 other fields	High correlation
`aware_sm_interact_score` is highly correlated with `study_1_add_info` and 4 other fields	High correlation
`study_1_ethic_acc` has 157 (31.5%) missing values	Missing
`study_1_conc` has 242 (48.5%) missing values	Missing
`study_1_add_info` has 407 (81.6%) missing values	Missing
`study_2_ethic_acc` has 173 (34.7%) missing values	Missing
`study_2_conc` has 225 (45.1%) missing values	Missing
`study_2_add_info` has 405 (81.2%) missing values	Missing
`study_3_ethic_acc` has 270 (54.1%) missing values	Missing
`study_3_conc` has 271 (54.3%) missing values	Missing
`study_3_add_info` has 423 (84.8%) missing values	Missing
`study_4_ethic_acc` has 180 (36.1%) missing values	Missing
`study_4_conc` has 246 (49.3%) missing values	Missing
`study_4_add_info` has 412 (82.6%) missing values	Missing
`design_add_fac` has 299 (59.9%) missing values	Missing
`rank_add_fac_1` has 418 (83.8%) missing values	Missing
`rank_add_fac_1_pos` has 350 (70.1%) missing values	Missing
`rank_add_fac_2` has 473 (94.8%) missing values	Missing
`rank_add_fac_2_pos` has 412 (82.6%) missing values	Missing
`rank_add_fac_3` has 477 (95.6%) missing values	Missing
`rank_add_fac_3_pos` has 417 (83.6%) missing values	Missing
`df_index` is uniformly distributed	Uniform
`ethic_appr` is uniformly distributed	Uniform
`study_1_conc` is uniformly distributed	Uniform
`study_1_add_info` is uniformly distributed	Uniform
`study_2_conc` is uniformly distributed	Uniform
`study_2_add_info` is uniformly distributed	Uniform
`study_3_conc` is uniformly distributed	Uniform
`study_3_add_info` is uniformly distributed	Uniform
`study_4_conc` is uniformly distributed	Uniform
`study_4_add_info` is uniformly distributed	Uniform
`design_add_fac` is uniformly distributed	Uniform
`df_index` has unique values	Unique
`aware_sm_advan` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`aware_sm_interact` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`aware_sm_use` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`rank_add_fac_1_pos` has 12 (2.4%) zeros	Zeros
`rank_add_fac_2_pos` has 11 (2.2%) zeros	Zeros
`rank_add_fac_3_pos` has 11 (2.2%) zeros	Zeros
`aware_sm_advan_score` has 47 (9.4%) zeros	Zeros
`aware_sm_use_score` has 5 (1.0%) zeros	Zeros

Reproduction

Analysis started	2022-11-21 12:14:43.854181
Analysis finished	2022-11-21 12:15:01.635959
Duration	17.78 seconds
Software version	pandas-profiling v3.4.0
Download configuration	config.json

df_index
Real number (ℝ_≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct	499
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	250

Minimum	1
Maximum	499
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	25.9
Q1	125.5
median	250
Q3	374.5
95-th percentile	474.1
Maximum	499
Range	498
Interquartile range (IQR)	249

Descriptive statistics

Standard deviation	144.1931575
Coefficient of variation (CV)	0.57677263
Kurtosis	-1.2
Mean	250
Median Absolute Deviation (MAD)	125
Skewness	0
Sum	124750
Variance	20791.66667
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	1	0.2%
329	1	0.2%
342	1	0.2%
341	1	0.2%
340	1	0.2%
339	1	0.2%
338	1	0.2%
337	1	0.2%
336	1	0.2%
335	1	0.2%
Other values (489)	489	98.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
3	1	0.2%
4	1	0.2%
5	1	0.2%
6	1	0.2%
7	1	0.2%
8	1	0.2%
9	1	0.2%
10	1	0.2%

Value	Count	Frequency (%)
499	1	0.2%
498	1	0.2%
497	1	0.2%
496	1	0.2%
495	1	0.2%
494	1	0.2%
493	1	0.2%
492	1	0.2%
491	1	0.2%
490	1	0.2%

sm_use
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Memory size	759.0 B

Facebook	258
Reddit	133
Twitter	108

Length

Max length	8
Median length	8
Mean length	7.250501002
Min length	6

Characters and Unicode

Total characters	3618
Distinct characters	14
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Facebook
2nd row	Twitter
3rd row	Facebook
4th row	Facebook
5th row	Twitter

Common Values

Value	Count	Frequency (%)
Facebook	258	51.7%
Reddit	133	26.7%
Twitter	108	21.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
facebook	258	51.7%
reddit	133	26.7%
twitter	108	21.6%

Most occurring characters

Value	Count	Frequency (%)
o	516	14.3%
e	499	13.8%
t	349	9.6%
d	266	7.4%
F	258	7.1%
a	258	7.1%
c	258	7.1%
b	258	7.1%
k	258	7.1%
i	241	6.7%
Other values (4)	457	12.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	3119	86.2%
Uppercase Letter	499	13.8%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
o	516	16.5%
e	499	16.0%
t	349	11.2%
d	266	8.5%
a	258	8.3%
c	258	8.3%
b	258	8.3%
k	258	8.3%
i	241	7.7%
w	108	3.5%

Uppercase Letter

Value	Count	Frequency (%)
F	258	51.7%
R	133	26.7%
T	108	21.6%

Most occurring scripts

Value	Count	Frequency (%)
Latin	3618	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
o	516	14.3%
e	499	13.8%
t	349	9.6%
d	266	7.4%
F	258	7.1%
a	258	7.1%
c	258	7.1%
b	258	7.1%
k	258	7.1%
i	241	6.7%
Other values (4)	457	12.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	3618	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
o	516	14.3%
e	499	13.8%
t	349	9.6%
d	266	7.4%
F	258	7.1%
a	258	7.1%
c	258	7.1%
b	258	7.1%
k	258	7.1%
i	241	6.7%
Other values (4)	457	12.6%

age
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	60
Distinct (%)	12.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	41.66332665

Minimum	18
Maximum	78
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	18
5-th percentile	23
Q1	31
median	39
Q3	51.5
95-th percentile	67
Maximum	78
Range	60
Interquartile range (IQR)	20.5

Descriptive statistics

Standard deviation	13.63593166
Coefficient of variation (CV)	0.3272885954
Kurtosis	-0.5585557113
Mean	41.66332665
Median Absolute Deviation (MAD)	10
Skewness	0.5655176939
Sum	20790
Variance	185.9386323
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
35	22	4.4%
34	20	4.0%
37	19	3.8%
29	18	3.6%
27	18	3.6%
26	17	3.4%
44	15	3.0%
31	15	3.0%
38	15	3.0%
23	14	2.8%
Other values (50)	326	65.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
18	1	0.2%
19	4	0.8%
20	3	0.6%
21	2	0.4%
22	2	0.4%
23	14	2.8%
24	8	1.6%
25	10	2.0%
26	17	3.4%
27	18	3.6%

Value	Count	Frequency (%)
78	1	0.2%
76	3	0.6%
75	1	0.2%
74	1	0.2%
73	2	0.4%
72	1	0.2%
71	2	0.4%
70	6	1.2%
69	3	0.6%
68	2	0.4%

gender_id
Categorical

HIGH CORRELATION

Distinct	4
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Memory size	831.0 B

Male	282
Female	207
Non-binary / third gender	8
Prefer not to say	2

Length

Max length	25
Median length	4
Mean length	5.218436874
Min length	4

Characters and Unicode

Total characters	2604
Distinct characters	23
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Male
2nd row	Male
3rd row	Female
4th row	Female
5th row	Female

Common Values

Value	Count	Frequency (%)
Male	282	56.5%
Female	207	41.5%
Non-binary / third gender	8	1.6%
Prefer not to say	2	0.4%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
male	282	53.3%
female	207	39.1%
non-binary	8	1.5%
	8	1.5%
third	8	1.5%
gender	8	1.5%
prefer	2	0.4%
not	2	0.4%
to	2	0.4%
say	2	0.4%

Most occurring characters

Value	Count	Frequency (%)
e	716	27.5%
a	499	19.2%
l	489	18.8%
M	282	10.8%
F	207	7.9%
m	207	7.9%
	30	1.2%
r	28	1.1%
n	26	1.0%
d	16	0.6%
Other values (13)	104	4.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	2059	79.1%
Uppercase Letter	499	19.2%
Space Separator	30	1.2%
Dash Punctuation	8	0.3%
Other Punctuation	8	0.3%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	716	34.8%
a	499	24.2%
l	489	23.7%
m	207	10.1%
r	28	1.4%
n	26	1.3%
d	16	0.8%
i	16	0.8%
o	12	0.6%
t	12	0.6%
Other values (6)	38	1.8%

Uppercase Letter

Value	Count	Frequency (%)
M	282	56.5%
F	207	41.5%
N	8	1.6%
P	2	0.4%

Space Separator

Value	Count	Frequency (%)
	30	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	8	100.0%

Other Punctuation

Value	Count	Frequency (%)
/	8	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2558	98.2%
Common	46	1.8%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	716	28.0%
a	499	19.5%
l	489	19.1%
M	282	11.0%
F	207	8.1%
m	207	8.1%
r	28	1.1%
n	26	1.0%
d	16	0.6%
i	16	0.6%
Other values (10)	72	2.8%

Common

Value	Count	Frequency (%)
	30	65.2%
-	8	17.4%
/	8	17.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2604	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	716	27.5%
a	499	19.2%
l	489	18.8%
M	282	10.8%
F	207	7.9%
m	207	7.9%
	30	1.2%
r	28	1.1%
n	26	1.0%
d	16	0.6%
Other values (13)	104	4.0%

ethnic_id
Categorical

HIGH CORRELATION

Distinct	12
Distinct (%)	2.4%
Missing	0
Missing (%)	0.0%
Memory size	1023.0 B

White / Caucasian	397
African-American	32
Mixed race	20
Hispanic	19
Asian - Eastern	16
Other values (7)	15

Length

Max length	17
Median length	17
Mean length	16.15230461
Min length	5

Characters and Unicode

Total characters	8060
Distinct characters	34
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	5 ?
Unique (%)	1.0%

Sample

1st row	Asian - Eastern
2nd row	Mixed race
3rd row	Pacific Islander
4th row	White / Caucasian
5th row	Native-American

Common Values

Value	Count	Frequency (%)
White / Caucasian	397	79.6%
African-American	32	6.4%
Mixed race	20	4.0%
Hispanic	19	3.8%
Asian - Eastern	16	3.2%
Asian - Indian	7	1.4%
Native-American	3	0.6%
Asian - Southeast	1	0.2%
Carribean	1	0.2%
Other	1	0.2%
Other values (2)	2	0.4%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
	421	30.8%
white	397	29.1%
caucasian	397	29.1%
african-american	32	2.3%
asian	24	1.8%
mixed	20	1.5%
race	20	1.5%
hispanic	19	1.4%
eastern	16	1.2%
indian	7	0.5%
Other values (10)	12	0.9%

Most occurring characters

Value	Count	Frequency (%)
a	1353	16.8%
i	956	11.9%
	866	10.7%
n	540	6.7%
c	505	6.3%
e	497	6.2%
s	459	5.7%
t	421	5.2%
h	399	5.0%
C	398	4.9%
Other values (24)	1666	20.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	5782	71.7%
Uppercase Letter	956	11.9%
Space Separator	866	10.7%
Other Punctuation	397	4.9%
Dash Punctuation	59	0.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	1353	23.4%
i	956	16.5%
n	540	9.3%
c	505	8.7%
e	497	8.6%
s	459	7.9%
t	421	7.3%
h	399	6.9%
u	398	6.9%
r	109	1.9%
Other values (10)	145	2.5%

Uppercase Letter

Value	Count	Frequency (%)
C	398	41.6%
W	397	41.5%
A	91	9.5%
M	20	2.1%
H	19	2.0%
E	16	1.7%
I	8	0.8%
N	3	0.3%
P	2	0.2%
S	1	0.1%

Space Separator

Value	Count	Frequency (%)
	866	100.0%

Other Punctuation

Value	Count	Frequency (%)
/	397	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	59	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	6738	83.6%
Common	1322	16.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	1353	20.1%
i	956	14.2%
n	540	8.0%
c	505	7.5%
e	497	7.4%
s	459	6.8%
t	421	6.2%
h	399	5.9%
C	398	5.9%
u	398	5.9%
Other values (21)	812	12.1%

Common

Value	Count	Frequency (%)
	866	65.5%
/	397	30.0%
-	59	4.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8060	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	1353	16.8%
i	956	11.9%
	866	10.7%
n	540	6.7%
c	505	6.3%
e	497	6.2%
s	459	5.7%
t	421	5.2%
h	399	5.0%
C	398	4.9%
Other values (24)	1666	20.7%

edu
Categorical

HIGH CORRELATION

Distinct	7
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Memory size	983.0 B

Bachelor's degree	222
Highschool	153
Master's degree or above	87
Associate's degree	22
Some college	7
Other values (2)	8

Length

Max length	24
Median length	19
Mean length	16.06412826
Min length	10

Characters and Unicode

Total characters	8016
Distinct characters	27
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Highschool
2nd row	Highschool
3rd row	Bachelor's degree
4th row	Highschool
5th row	Highschool

Common Values

Value	Count	Frequency (%)
Bachelor's degree	222	44.5%
Highschool	153	30.7%
Master's degree or above	87	17.4%
Associate's degree	22	4.4%
Some college	7	1.4%
Prefer not to say	4	0.8%
Vocational training	4	0.8%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
degree	331	32.2%
bachelor's	222	21.6%
highschool	153	14.9%
master's	87	8.5%
or	87	8.5%
above	87	8.5%
associate's	22	2.1%
some	7	0.7%
college	7	0.7%
prefer	4	0.4%
Other values (5)	20	1.9%

Most occurring characters

Value	Count	Frequency (%)
e	1440	18.0%
o	754	9.4%
r	739	9.2%
s	619	7.7%
h	528	6.6%
	528	6.6%
g	495	6.2%
a	434	5.4%
c	408	5.1%
l	393	4.9%
Other values (17)	1678	20.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	6658	83.1%
Space Separator	528	6.6%
Uppercase Letter	499	6.2%
Other Punctuation	331	4.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1440	21.6%
o	754	11.3%
r	739	11.1%
s	619	9.3%
h	528	7.9%
g	495	7.4%
a	434	6.5%
c	408	6.1%
l	393	5.9%
d	331	5.0%
Other values (8)	517	7.8%

Uppercase Letter

Value	Count	Frequency (%)
B	222	44.5%
H	153	30.7%
M	87	17.4%
A	22	4.4%
S	7	1.4%
P	4	0.8%
V	4	0.8%

Space Separator

Value	Count	Frequency (%)
	528	100.0%

Other Punctuation

Value	Count	Frequency (%)
'	331	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	7157	89.3%
Common	859	10.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1440	20.1%
o	754	10.5%
r	739	10.3%
s	619	8.6%
h	528	7.4%
g	495	6.9%
a	434	6.1%
c	408	5.7%
l	393	5.5%
d	331	4.6%
Other values (15)	1016	14.2%

Common

Value	Count	Frequency (%)
	528	61.5%
'	331	38.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8016	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	1440	18.0%
o	754	9.4%
r	739	9.2%
s	619	7.7%
h	528	6.6%
	528	6.6%
g	495	6.2%
a	434	5.4%
c	408	5.1%
l	393	4.9%
Other values (17)	1678	20.9%

politic_views
Categorical

HIGH CORRELATION

Distinct	6
Distinct (%)	1.2%
Missing	0
Missing (%)	0.0%
Memory size	847.0 B

Very liberal	150
Slightly liberal	126
Slightly conservative	96
Neutral/ Neither conservative or liberal	89
Very conservative	35

Length

Max length	40
Median length	21
Mean length	20.11623246
Min length	12

Characters and Unicode

Total characters	10038
Distinct characters	23
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Slightly liberal
2nd row	Neutral/ Neither conservative or liberal
3rd row	Very liberal
4th row	Slightly conservative
5th row	Very liberal

Common Values

Value	Count	Frequency (%)
Very liberal	150	30.1%
Slightly liberal	126	25.3%
Slightly conservative	96	19.2%
Neutral/ Neither conservative or liberal	89	17.8%
Very conservative	35	7.0%
Prefer not to say	3	0.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
liberal	365	28.7%
slightly	222	17.5%
conservative	220	17.3%
very	185	14.6%
neutral	89	7.0%
neither	89	7.0%
or	89	7.0%
prefer	3	0.2%
not	3	0.2%
to	3	0.2%

Most occurring characters

Value	Count	Frequency (%)
e	1263	12.6%
l	1263	12.6%
r	1043	10.4%
i	896	8.9%
	772	7.7%
a	677	6.7%
t	626	6.2%
v	440	4.4%
y	410	4.1%
b	365	3.6%
Other values (13)	2283	22.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	8589	85.6%
Space Separator	772	7.7%
Uppercase Letter	588	5.9%
Other Punctuation	89	0.9%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1263	14.7%
l	1263	14.7%
r	1043	12.1%
i	896	10.4%
a	677	7.9%
t	626	7.3%
v	440	5.1%
y	410	4.8%
b	365	4.2%
o	315	3.7%
Other values (7)	1291	15.0%

Uppercase Letter

Value	Count	Frequency (%)
S	222	37.8%
V	185	31.5%
N	178	30.3%
P	3	0.5%

Space Separator

Value	Count	Frequency (%)
	772	100.0%

Other Punctuation

Value	Count	Frequency (%)
/	89	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	9177	91.4%
Common	861	8.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1263	13.8%
l	1263	13.8%
r	1043	11.4%
i	896	9.8%
a	677	7.4%
t	626	6.8%
v	440	4.8%
y	410	4.5%
b	365	4.0%
o	315	3.4%
Other values (11)	1879	20.5%

Common

Value	Count	Frequency (%)
	772	89.7%
/	89	10.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	10038	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	1263	12.6%
l	1263	12.6%
r	1043	10.4%
i	896	8.9%
	772	7.7%
a	677	6.7%
t	626	6.2%
v	440	4.4%
y	410	4.1%
b	365	3.6%
Other values (13)	2283	22.7%

aware_sm_res
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	839.0 B

Moderately aware	128
Very aware	119
Slightly aware	117
Not at all aware	76
Extremely aware	59

Length

Max length	16
Median length	15
Mean length	13.98196393
Min length	10

Characters and Unicode

Total characters	6977
Distinct characters	20
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Extremely aware
2nd row	Moderately aware
3rd row	Extremely aware
4th row	Moderately aware
5th row	Extremely aware

Common Values

Value	Count	Frequency (%)
Moderately aware	128	25.7%
Very aware	119	23.8%
Slightly aware	117	23.4%
Not at all aware	76	15.2%
Extremely aware	59	11.8%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
aware	499	43.4%
moderately	128	11.1%
very	119	10.3%
slightly	117	10.2%
not	76	6.6%
at	76	6.6%
all	76	6.6%
extremely	59	5.1%

Most occurring characters

Value	Count	Frequency (%)
a	1278	18.3%
e	992	14.2%
r	805	11.5%
	651	9.3%
l	573	8.2%
w	499	7.2%
t	456	6.5%
y	423	6.1%
o	204	2.9%
M	128	1.8%
Other values (10)	968	13.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	5827	83.5%
Space Separator	651	9.3%
Uppercase Letter	499	7.2%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	1278	21.9%
e	992	17.0%
r	805	13.8%
l	573	9.8%
w	499	8.6%
t	456	7.8%
y	423	7.3%
o	204	3.5%
d	128	2.2%
i	117	2.0%
Other values (4)	352	6.0%

Uppercase Letter

Value	Count	Frequency (%)
M	128	25.7%
V	119	23.8%
S	117	23.4%
N	76	15.2%
E	59	11.8%

Space Separator

Value	Count	Frequency (%)
	651	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	6326	90.7%
Common	651	9.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	1278	20.2%
e	992	15.7%
r	805	12.7%
l	573	9.1%
w	499	7.9%
t	456	7.2%
y	423	6.7%
o	204	3.2%
M	128	2.0%
d	128	2.0%
Other values (9)	840	13.3%

Common

Value	Count	Frequency (%)
	651	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	6977	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	1278	18.3%
e	992	14.2%
r	805	11.5%
	651	9.3%
l	573	8.2%
w	499	7.2%
t	456	6.5%
y	423	6.1%
o	204	2.9%
M	128	1.8%
Other values (10)	968	13.9%

aware_sm_advan
Unsupported

REJECTED
UNSUPPORTED

Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

aware_sm_interact
Unsupported

REJECTED
UNSUPPORTED

Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

aware_sm_use
Unsupported

REJECTED
UNSUPPORTED

Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

ethic_appr
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	498
Distinct (%)	99.8%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

Ethics approval is needed for any research that involves human participants; their tissue and /or data to ensure that the dignity, rights, safety and well-being of all participants are the primary consideration of the research project.	2
The scope of the project and actions there in do not cross certain boundaries that may purposefully negatively affect participants as well as legal regulations and standard practices.	1
That they are going to use the information they receive appropriately. They are not going to manipulate and misuse what they gather.	1
It means that, in the opinion of the institution, the study and its methods are morally acceptable.	1
Ethical approval means getting clearance to obtain data from a research subject.	1
Other values (493)	493

Length

Max length	1026
Median length	207
Mean length	134.7935872
Min length	15

Characters and Unicode

Total characters	67262
Distinct characters	66
Distinct categories	10 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	497 ?
Unique (%)	99.6%

Sample

1st row	The scope of the project and actions there in do not cross certain boundaries that may purposefully negatively affect participants as well as legal regulations and standard practices.
2nd row	I think Ethical Approval means that the experiment is gathering data without harm or injury to people.
3rd row	Researchers focus on ethical standards towards those they gain data from. They need approval of their approach and receive methods.
4th row	I would think that using "ethical approval" means that the things others collect on social media sites would need to be honest and moral. Hopefully, there would be no under-handedness used in collecting information.
5th row	A set of rules of what to do and what to not do.

Common Values

Value	Count	Frequency (%)
Ethics approval is needed for any research that involves human participants; their tissue and /or data to ensure that the dignity, rights, safety and well-being of all participants are the primary consideration of the research project.	2	0.4%
The scope of the project and actions there in do not cross certain boundaries that may purposefully negatively affect participants as well as legal regulations and standard practices.	1	0.2%
That they are going to use the information they receive appropriately. They are not going to manipulate and misuse what they gather.	1	0.2%
It means that, in the opinion of the institution, the study and its methods are morally acceptable.	1	0.2%
Ethical approval means getting clearance to obtain data from a research subject.	1	0.2%
It means receiving approval from an IRB or other institution that has oversight over study approval. They make sure the studies to not hamr their subjects.	1	0.2%
Ethical approval from the institution means they will act in a way that responsible and takes in to account the persons they are researching.	1	0.2%
Proof that the experiment is not done against people's wills and if people ask, all data will be deleted.	1	0.2%
There is (or should be ) oversight from someone in charge, and who is ethical.	1	0.2%
Morally correct.	1	0.2%
Other values (488)	488	97.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	736	6.5%
to	474	4.2%
that	434	3.8%
and	295	2.6%
is	249	2.2%
ethical	249	2.2%
of	227	2.0%
it	217	1.9%
they	212	1.9%
approval	201	1.8%
Other values (1400)	8064	71.0%

Most occurring characters

Value	Count	Frequency (%)
	11017	16.4%
e	6612	9.8%
t	6115	9.1%
a	4947	7.4%
i	4019	6.0%
o	3823	5.7%
n	3645	5.4%
s	3457	5.1%
r	3449	5.1%
h	3300	4.9%
Other values (56)	16878	25.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	54478	81.0%
Space Separator	11017	16.4%
Other Punctuation	966	1.4%
Uppercase Letter	705	1.0%
Dash Punctuation	36	0.1%
Open Punctuation	22	< 0.1%
Close Punctuation	22	< 0.1%
Final Punctuation	8	< 0.1%
Control	7	< 0.1%
Decimal Number	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	6612	12.1%
t	6115	11.2%
a	4947	9.1%
i	4019	7.4%
o	3823	7.0%
n	3645	6.7%
s	3457	6.3%
r	3449	6.3%
h	3300	6.1%
l	2057	3.8%
Other values (16)	13054	24.0%

Uppercase Letter

Value	Count	Frequency (%)
I	257	36.5%
T	144	20.4%
E	91	12.9%
A	46	6.5%
B	24	3.4%
R	23	3.3%
M	19	2.7%
W	15	2.1%
P	9	1.3%
S	9	1.3%
Other values (15)	68	9.6%

Other Punctuation

Value	Count	Frequency (%)
.	535	55.4%
,	234	24.2%
'	141	14.6%
"	28	2.9%
/	12	1.2%
?	10	1.0%
;	4	0.4%
:	2	0.2%

Space Separator

Value	Count	Frequency (%)
	11017	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	36	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	22	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	22	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	8	100.0%

Control

Value	Count	Frequency (%)
	7	100.0%

Decimal Number

Value	Count	Frequency (%)
3	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	55183	82.0%
Common	12079	18.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	6612	12.0%
t	6115	11.1%
a	4947	9.0%
i	4019	7.3%
o	3823	6.9%
n	3645	6.6%
s	3457	6.3%
r	3449	6.3%
h	3300	6.0%
l	2057	3.7%
Other values (41)	13759	24.9%

Common

Value	Count	Frequency (%)
	11017	91.2%
.	535	4.4%
,	234	1.9%
'	141	1.2%
-	36	0.3%
"	28	0.2%
(	22	0.2%
)	22	0.2%
/	12	0.1%
?	10	0.1%
Other values (5)	22	0.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	67254	> 99.9%
Punctuation	8	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	11017	16.4%
e	6612	9.8%
t	6115	9.1%
a	4947	7.4%
i	4019	6.0%
o	3823	5.7%
n	3645	5.4%
s	3457	5.1%
r	3449	5.1%
h	3300	4.9%
Other values (55)	16870	25.1%

Punctuation

Value	Count	Frequency (%)
’	8	100.0%

study_1_ethic_acc
Categorical

HIGH CORRELATION
MISSING

Distinct	4
Distinct (%)	1.2%
Missing	157
Missing (%)	31.5%
Memory size	839.0 B

Somewhat acceptable	144
Somewhat unacceptable	81
Neutral	62
Completey unacceptable	55

Length

Max length	22
Median length	21
Mean length	17.78070175
Min length	7

Characters and Unicode

Total characters	6081
Distinct characters	19
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Neutral
2nd row	Neutral
3rd row	Somewhat acceptable
4th row	Completey unacceptable
5th row	Neutral

Common Values

Value	Count	Frequency (%)
Somewhat acceptable	144	28.9%
Somewhat unacceptable	81	16.2%
Neutral	62	12.4%
Completey unacceptable	55	11.0%
(Missing)	157	31.5%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
somewhat	225	36.2%
acceptable	144	23.2%
unacceptable	136	21.9%
neutral	62	10.0%
completey	55	8.8%

Most occurring characters

Value	Count	Frequency (%)
e	957	15.7%
a	847	13.9%
t	622	10.2%
c	560	9.2%
l	397	6.5%
p	335	5.5%
m	280	4.6%
b	280	4.6%
	280	4.6%
o	280	4.6%
Other values (9)	1243	20.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	5459	89.8%
Uppercase Letter	342	5.6%
Space Separator	280	4.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	957	17.5%
a	847	15.5%
t	622	11.4%
c	560	10.3%
l	397	7.3%
p	335	6.1%
m	280	5.1%
b	280	5.1%
o	280	5.1%
h	225	4.1%
Other values (5)	676	12.4%

Uppercase Letter

Value	Count	Frequency (%)
S	225	65.8%
N	62	18.1%
C	55	16.1%

Space Separator

Value	Count	Frequency (%)
	280	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	5801	95.4%
Common	280	4.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	957	16.5%
a	847	14.6%
t	622	10.7%
c	560	9.7%
l	397	6.8%
p	335	5.8%
m	280	4.8%
b	280	4.8%
o	280	4.8%
S	225	3.9%
Other values (8)	1018	17.5%

Common

Value	Count	Frequency (%)
	280	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	6081	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	957	15.7%
a	847	13.9%
t	622	10.2%
c	560	9.2%
l	397	6.5%
p	335	5.5%
m	280	4.6%
b	280	4.6%
	280	4.6%
o	280	4.6%
Other values (9)	1243	20.4%

study_1_conc
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct	253
Distinct (%)	98.4%
Missing	242
Missing (%)	48.5%
Memory size	4.0 KiB

None	4
participants were not aware they were part of a research study	2
the anonymity of twitter already draws an inordinate amount of these kinds of posters	1
again, not letting the twitter user that their data is being used does not seem honest and above boardi even have a hard time with the ethics of the researchers on prolific when they state, "we're sorry, but we had to deceive you", it reeks of manipulation that i will never understand is needed i am sure it is "for the greater good", but it still feels dishonest and not trustworthy	1
once again, users were not informed that they were part of a studysecondly, based on my experience from reading hate speech, i seriously doubt that a majority of people deleted their "hate" post after reading an empathetic response from a researcher the posts that i most often see deleted are those where someone has posted a reply that shows that the original poster misinterpreted some facts and were provided with correct info the rest of them often enjoy their roles as trolls and do not bother deleting much of anything	1
Other values (248)	248

Length

Max length	809
Median length	182
Mean length	132.3774319
Min length	4

Characters and Unicode

Total characters	34021
Distinct characters	45
Distinct categories	9 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	251 ?
Unique (%)	97.7%

Sample

1st row	no concerns i would have loved to partake in this study in terms of watching the results
2nd row	i feel if people know they are being judged they will act, speak, or write differently than if they don't know they are being analyzed
3rd row	easy enough for an outside government to try copying such a study with the sole purpose of creating much more polarization, hate, etc not that it hasn't been tried and tested perhaps innumerable times by all types of foreign or domestic entities as far as we know no actual study would have really been needed to know that using a type of marketing manipulation could alter the recipients moodlevels of concernanxietyhateetc
4th row	the participants were not were of this research study being conducted therefore it is unethical
5th row	deceitful and lazy even with the positive result how you get results matter

Common Values

Value	Count	Frequency (%)
None	4	0.8%
participants were not aware they were part of a research study	2	0.4%
the anonymity of twitter already draws an inordinate amount of these kinds of posters	1	0.2%
again, not letting the twitter user that their data is being used does not seem honest and above boardi even have a hard time with the ethics of the researchers on prolific when they state, "we're sorry, but we had to deceive you", it reeks of manipulation that i will never understand is needed i am sure it is "for the greater good", but it still feels dishonest and not trustworthy	1	0.2%
once again, users were not informed that they were part of a studysecondly, based on my experience from reading hate speech, i seriously doubt that a majority of people deleted their "hate" post after reading an empathetic response from a researcher the posts that i most often see deleted are those where someone has posted a reply that shows that the original poster misinterpreted some facts and were provided with correct info the rest of them often enjoy their roles as trolls and do not bother deleting much of anything	1	0.2%
although i do not feel like this is a huge ethical issue, i do feel that any type of misleading intentionally might sit on an ethical borderline	1	0.2%
using people's data without consent for a study seems unethical	1	0.2%
same as before, this is all public and anyone can do these things, so i have no issue with it	1	0.2%
in order for the study's results to be accurate users couldn't know that the researchers were running a study	1	0.2%
i feel its quit acceptable posting something that reduces the hate in general also since it helps one to rethink their post	1	0.2%
Other values (243)	243	48.7%
(Missing)	242	48.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	296	4.9%
to	158	2.6%
that	156	2.6%
i	148	2.5%
a	141	2.3%
of	134	2.2%
is	125	2.1%
they	120	2.0%
study	107	1.8%
not	106	1.8%
Other values (1068)	4546	75.3%

Most occurring characters

Value	Count	Frequency (%)
	5808	17.1%
e	3735	11.0%
t	3128	9.2%
a	2221	6.5%
i	2113	6.2%
o	1871	5.5%
s	1834	5.4%
n	1794	5.3%
h	1584	4.7%
r	1488	4.4%
Other values (35)	8445	24.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	27813	81.8%
Space Separator	5808	17.1%
Other Punctuation	352	1.0%
Dash Punctuation	13	< 0.1%
Decimal Number	9	< 0.1%
Close Punctuation	8	< 0.1%
Final Punctuation	7	< 0.1%
Open Punctuation	7	< 0.1%
Uppercase Letter	4	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	3735	13.4%
t	3128	11.2%
a	2221	8.0%
i	2113	7.6%
o	1871	6.7%
s	1834	6.6%
n	1794	6.5%
h	1584	5.7%
r	1488	5.4%
d	982	3.5%
Other values (16)	7063	25.4%

Other Punctuation

Value	Count	Frequency (%)
,	161	45.7%
'	128	36.4%
"	38	10.8%
?	16	4.5%
!	5	1.4%
:	3	0.9%
;	1	0.3%

Decimal Number

Value	Count	Frequency (%)
0	5	55.6%
2	2	22.2%
1	1	11.1%
4	1	11.1%

Close Punctuation

Value	Count	Frequency (%)
)	6	75.0%
]	2	25.0%

Open Punctuation

Value	Count	Frequency (%)
(	5	71.4%
[	2	28.6%

Space Separator

Value	Count	Frequency (%)
	5808	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	13	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	7	100.0%

Uppercase Letter

Value	Count	Frequency (%)
N	4	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	27817	81.8%
Common	6204	18.2%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	3735	13.4%
t	3128	11.2%
a	2221	8.0%
i	2113	7.6%
o	1871	6.7%
s	1834	6.6%
n	1794	6.4%
h	1584	5.7%
r	1488	5.3%
d	982	3.5%
Other values (17)	7067	25.4%

Common

Value	Count	Frequency (%)
	5808	93.6%
,	161	2.6%
'	128	2.1%
"	38	0.6%
?	16	0.3%
-	13	0.2%
’	7	0.1%
)	6	0.1%
0	5	0.1%
(	5	0.1%
Other values (8)	17	0.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	34014	> 99.9%
Punctuation	7	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	5808	17.1%
e	3735	11.0%
t	3128	9.2%
a	2221	6.5%
i	2113	6.2%
o	1871	5.5%
s	1834	5.4%
n	1794	5.3%
h	1584	4.7%
r	1488	4.4%
Other values (34)	8438	24.8%

Punctuation

Value	Count	Frequency (%)
’	7	100.0%

study_1_add_info
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Distinct	90
Distinct (%)	97.8%
Missing	407
Missing (%)	81.6%
Memory size	4.0 KiB

None	3
the fact that the fake accounts were used to try and suppress hate speech, makes it more ethical in my opinion	1
i would love to see the message that was posted by the researches to what was posted in order to see what was said and how it was worded	1
no one is getting harmed or misinformed here the study is actually trying to help people, so i think it's acceptable even though they did not know they were in a study	1
i would like to know more about what the replies actually said	1
Other values (85)	85

Length

Max length	312
Median length	112.5
Mean length	97.91304348
Min length	4

Characters and Unicode

Total characters	9008
Distinct characters	40
Distinct categories	9 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	89 ?
Unique (%)	96.7%

Sample

1st row	i would be interested to know what kind of messages they sent the hate speech users that got them to change their minds
2nd row	full disclosure of intent of researchers
3rd row	see comments from previous studies
4th row	it kind of depends on what the replies were i guess i'm not 100% sure if it is ethically acceptable because i don't know what the researchers said i would assume it was ok though
5th row	interesting idea for a study i'd have to think long and hard about its value i no longer use twitter

Common Values

Value	Count	Frequency (%)
None	3	0.6%
the fact that the fake accounts were used to try and suppress hate speech, makes it more ethical in my opinion	1	0.2%
i would love to see the message that was posted by the researches to what was posted in order to see what was said and how it was worded	1	0.2%
no one is getting harmed or misinformed here the study is actually trying to help people, so i think it's acceptable even though they did not know they were in a study	1	0.2%
i would like to know more about what the replies actually said	1	0.2%
i would want to know the various content of the messages created by the researchers	1	0.2%
people in the study should have been aware or notified people before going forward with the study	1	0.2%
no, no more information is needed the researchers lied, period	1	0.2%
the results are illuminating in so far as it goes, but i would like to know how long lasting the effect of the empathetic messages were just the 4 days, longer, permanent change?	1	0.2%
if users gave their consent before their data was published, this would be totally acceptable	1	0.2%
Other values (80)	80	16.0%
(Missing)	407	81.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	93	5.8%
to	73	4.5%
i	44	2.7%
it	33	2.1%
they	33	2.1%
of	32	2.0%
would	28	1.7%
were	26	1.6%
if	23	1.4%
was	23	1.4%
Other values (483)	1200	74.6%

Most occurring characters

Value	Count	Frequency (%)
	1532	17.0%
e	1038	11.5%
t	806	8.9%
o	555	6.2%
a	545	6.1%
i	511	5.7%
s	502	5.6%
n	436	4.8%
h	414	4.6%
r	376	4.2%
Other values (30)	2293	25.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7362	81.7%
Space Separator	1532	17.0%
Other Punctuation	80	0.9%
Decimal Number	12	0.1%
Dash Punctuation	6	0.1%
Open Punctuation	6	0.1%
Close Punctuation	6	0.1%
Uppercase Letter	3	< 0.1%
Final Punctuation	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1038	14.1%
t	806	10.9%
o	555	7.5%
a	545	7.4%
i	511	6.9%
s	502	6.8%
n	436	5.9%
h	414	5.6%
r	376	5.1%
l	272	3.7%
Other values (16)	1907	25.9%

Other Punctuation

Value	Count	Frequency (%)
,	44	55.0%
'	22	27.5%
?	11	13.8%
"	2	2.5%
%	1	1.2%

Decimal Number

Value	Count	Frequency (%)
0	8	66.7%
1	3	25.0%
4	1	8.3%

Space Separator

Value	Count	Frequency (%)
	1532	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	6	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	6	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	6	100.0%

Uppercase Letter

Value	Count	Frequency (%)
N	3	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	7365	81.8%
Common	1643	18.2%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1038	14.1%
t	806	10.9%
o	555	7.5%
a	545	7.4%
i	511	6.9%
s	502	6.8%
n	436	5.9%
h	414	5.6%
r	376	5.1%
l	272	3.7%
Other values (17)	1910	25.9%

Common

Value	Count	Frequency (%)
	1532	93.2%
,	44	2.7%
'	22	1.3%
?	11	0.7%
0	8	0.5%
-	6	0.4%
(	6	0.4%
)	6	0.4%
1	3	0.2%
"	2	0.1%
Other values (3)	3	0.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9007	> 99.9%
Punctuation	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1532	17.0%
e	1038	11.5%
t	806	8.9%
o	555	6.2%
a	545	6.1%
i	511	5.7%
s	502	5.6%
n	436	4.8%
h	414	4.6%
r	376	4.2%
Other values (29)	2292	25.4%

Punctuation

Value	Count	Frequency (%)
’	1	100.0%

study_2_ethic_acc
Categorical

HIGH CORRELATION
MISSING

Distinct	3
Distinct (%)	0.9%
Missing	173
Missing (%)	34.7%
Memory size	839.0 B

Somewhat acceptable	133
Somewhat unacceptable	111
Neutral	82

Length

Max length	21
Median length	19
Mean length	16.66257669
Min length	7

Characters and Unicode

Total characters	5432
Distinct characters	17
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Neutral
2nd row	Somewhat acceptable
3rd row	Somewhat unacceptable
4th row	Somewhat acceptable
5th row	Neutral

Common Values

Value	Count	Frequency (%)
Somewhat acceptable	133	26.7%
Somewhat unacceptable	111	22.2%
Neutral	82	16.4%
(Missing)	173	34.7%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
somewhat	244	42.8%
acceptable	133	23.3%
unacceptable	111	19.5%
neutral	82	14.4%

Most occurring characters

Value	Count	Frequency (%)
e	814	15.0%
a	814	15.0%
t	570	10.5%
c	488	9.0%
l	326	6.0%
S	244	4.5%
b	244	4.5%
p	244	4.5%
	244	4.5%
o	244	4.5%
Other values (7)	1200	22.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	4862	89.5%
Uppercase Letter	326	6.0%
Space Separator	244	4.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	814	16.7%
a	814	16.7%
t	570	11.7%
c	488	10.0%
l	326	6.7%
b	244	5.0%
p	244	5.0%
o	244	5.0%
h	244	5.0%
w	244	5.0%
Other values (4)	630	13.0%

Uppercase Letter

Value	Count	Frequency (%)
S	244	74.8%
N	82	25.2%

Space Separator

Value	Count	Frequency (%)
	244	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	5188	95.5%
Common	244	4.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	814	15.7%
a	814	15.7%
t	570	11.0%
c	488	9.4%
l	326	6.3%
S	244	4.7%
b	244	4.7%
p	244	4.7%
o	244	4.7%
h	244	4.7%
Other values (6)	956	18.4%

Common

Value	Count	Frequency (%)
	244	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	5432	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	814	15.0%
a	814	15.0%
t	570	10.5%
c	488	9.0%
l	326	6.0%
S	244	4.5%
b	244	4.5%
p	244	4.5%
	244	4.5%
o	244	4.5%
Other values (7)	1200	22.1%

study_2_conc
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct	274
Distinct (%)	100.0%
Missing	225
Missing (%)	45.1%
Memory size	4.0 KiB

they probably should have been told at some point that it was a research study	1
using bots on unaware citizens without their consent would seem to be unethical not to mention the famous case of the linkedin founder funding the troll farm "research" to manipulate the alabama special election unethical if not criminal	1
i understand that when posting on social media, you content is at the will of everyone but, i feel if researcher or analytics are going to be performed against your inputs, you should be notified	1
participants were uninformed the idea of private messaging someone is also bothersome to me	1
in order to get accurate results for the study, the users couldn't know that they were being surveyed	1
Other values (269)	269

Length

Max length	640
Median length	181.5
Mean length	124.7956204
Min length	32

Characters and Unicode

Total characters	34194
Distinct characters	41
Distinct categories	8 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	274 ?
Unique (%)	100.0%

Sample

1st row	going to the poster privately provided opportunity for change without the possibly of increased toxicity from users i prefer this method over commenting the "correct information"
2nd row	i feel as though, in the above case, users had a choice to respond or not so i think it was honest
3rd row	it's perfectly within someone's right to send someone else a message on any platform, therefore i believe this study was acceptable
4th row	this is unethical because those involved were not adequately informed of the researchers intent
5th row	it’s deception no matter how you look at it and it’s lazy as twitter users are not a representative sample of reality

Common Values

Value	Count	Frequency (%)
they probably should have been told at some point that it was a research study	1	0.2%
using bots on unaware citizens without their consent would seem to be unethical not to mention the famous case of the linkedin founder funding the troll farm "research" to manipulate the alabama special election unethical if not criminal	1	0.2%
i understand that when posting on social media, you content is at the will of everyone but, i feel if researcher or analytics are going to be performed against your inputs, you should be notified	1	0.2%
participants were uninformed the idea of private messaging someone is also bothersome to me	1	0.2%
in order to get accurate results for the study, the users couldn't know that they were being surveyed	1	0.2%
again, these researchers are just observing public activity everything they did was allowable by anyone, so i have no issues with this	1	0.2%
i feel like because the twitter users were intentionally mislead and the fact that it was a private message makes it less ethical	1	0.2%
the users were not aware that they were part of the study and very importantly, the study was limited to conservative users only i find that objectionable and wide open for manipulating data and drawing misleading conclusions	1	0.2%
a private message doesn't require the user to respond, they can simply ignore if they are not interested in sharing their opinion there's no harm in asking somebody their opinion, but the information gathered needs to be anonymous	1	0.2%
i really do not like tricks or deception on any level	1	0.2%
Other values (264)	264	52.9%
(Missing)	225	45.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	285	4.8%
to	170	2.8%
i	142	2.4%
a	142	2.4%
of	135	2.3%
they	132	2.2%
that	125	2.1%
not	122	2.0%
is	102	1.7%
study	102	1.7%
Other values (1022)	4508	75.6%

Most occurring characters

Value	Count	Frequency (%)
	5722	16.7%
e	3651	10.7%
t	3083	9.0%
a	2255	6.6%
i	2193	6.4%
s	1951	5.7%
n	1944	5.7%
o	1926	5.6%
r	1576	4.6%
h	1476	4.3%
Other values (31)	8417	24.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	28083	82.1%
Space Separator	5722	16.7%
Other Punctuation	333	1.0%
Dash Punctuation	32	0.1%
Close Punctuation	9	< 0.1%
Open Punctuation	8	< 0.1%
Final Punctuation	4	< 0.1%
Decimal Number	3	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	3651	13.0%
t	3083	11.0%
a	2255	8.0%
i	2193	7.8%
s	1951	6.9%
n	1944	6.9%
o	1926	6.9%
r	1576	5.6%
h	1476	5.3%
d	996	3.5%
Other values (16)	7032	25.0%

Other Punctuation

Value	Count	Frequency (%)
,	142	42.6%
'	131	39.3%
"	34	10.2%
?	21	6.3%
;	3	0.9%
!	1	0.3%
:	1	0.3%

Decimal Number

Value	Count	Frequency (%)
2	1	33.3%
4	1	33.3%
1	1	33.3%

Space Separator

Value	Count	Frequency (%)
	5722	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	32	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	9	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	8	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	4	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	28083	82.1%
Common	6111	17.9%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	3651	13.0%
t	3083	11.0%
a	2255	8.0%
i	2193	7.8%
s	1951	6.9%
n	1944	6.9%
o	1926	6.9%
r	1576	5.6%
h	1476	5.3%
d	996	3.5%
Other values (16)	7032	25.0%

Common

Value	Count	Frequency (%)
	5722	93.6%
,	142	2.3%
'	131	2.1%
"	34	0.6%
-	32	0.5%
?	21	0.3%
)	9	0.1%
(	8	0.1%
’	4	0.1%
;	3	< 0.1%
Other values (5)	5	0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	34190	> 99.9%
Punctuation	4	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	5722	16.7%
e	3651	10.7%
t	3083	9.0%
a	2255	6.6%
i	2193	6.4%
s	1951	5.7%
n	1944	5.7%
o	1926	5.6%
r	1576	4.6%
h	1476	4.3%
Other values (30)	8413	24.6%

Punctuation

Value	Count	Frequency (%)
’	4	100.0%

study_2_add_info
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Distinct	91
Distinct (%)	96.8%
Missing	405
Missing (%)	81.2%
Memory size	4.0 KiB

None	4
telling the users about the study after it was completed would have made it a bit more ethical	1
i feel that the individuals who were messaged by the researches should be informed of why they are being contacted, and that information that they provide will be used in an academic study if this is done, then fair play, but it seems that was not done here	1
the part about not telling people that they are part of a research study because misinformation is spread too much and now the participants will likely believe things that are untrue	1
of course there is always the concern about funding and bias i wonder if because of the private messaging whether there was any supplemental back-and-forth between the unwilling participant and someone behind the study which has potential for some form of abuse or corruption of data	1
Other values (86)	86

Length

Max length	399
Median length	139.5
Mean length	99.82978723
Min length	4

Characters and Unicode

Total characters	9384
Distinct characters	42
Distinct categories	9 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	90 ?
Unique (%)	95.7%

Sample

1st row	concerns over the possibility of the researchers having their own political agenda yet fake news is a major problem what social media really is when mass sharing news (political news), is simple propaganda from the left and right
2nd row	full disclosure if research intent
3rd row	how do you pick a representative sample on a non representative platform
4th row	just informing people about the study
5th row	if they used deceiving or untruthful tactics or anything of that nature

Common Values

Value	Count	Frequency (%)
None	4	0.8%
telling the users about the study after it was completed would have made it a bit more ethical	1	0.2%
i feel that the individuals who were messaged by the researches should be informed of why they are being contacted, and that information that they provide will be used in an academic study if this is done, then fair play, but it seems that was not done here	1	0.2%
the part about not telling people that they are part of a research study because misinformation is spread too much and now the participants will likely believe things that are untrue	1	0.2%
of course there is always the concern about funding and bias i wonder if because of the private messaging whether there was any supplemental back-and-forth between the unwilling participant and someone behind the study which has potential for some form of abuse or corruption of data	1	0.2%
i'd like to know the exact messages they are sending	1	0.2%
you need people consent to do a study on them	1	0.2%
if i found out who labeled the misinformation(same people who labeled hunter's laptop?) and if i found out that left leaning were also studied	1	0.2%
they aren't deceiving the users, they just aren't keeping them informed	1	0.2%
i understand the need to not inform participants and i suppose since all information gathered was in a public shared space there is no presumption of privacy	1	0.2%
Other values (81)	81	16.2%
(Missing)	405	81.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	99	6.0%
i	45	2.7%
to	39	2.4%
of	36	2.2%
they	36	2.2%
a	35	2.1%
if	34	2.1%
would	31	1.9%
that	30	1.8%
study	27	1.6%
Other values (502)	1230	74.9%

Most occurring characters

Value	Count	Frequency (%)
	1556	16.6%
e	993	10.6%
t	813	8.7%
a	598	6.4%
i	590	6.3%
o	536	5.7%
s	524	5.6%
n	483	5.1%
r	433	4.6%
h	403	4.3%
Other values (32)	2455	26.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7722	82.3%
Space Separator	1556	16.6%
Other Punctuation	68	0.7%
Dash Punctuation	10	0.1%
Open Punctuation	8	0.1%
Close Punctuation	8	0.1%
Decimal Number	7	0.1%
Uppercase Letter	4	< 0.1%
Final Punctuation	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	993	12.9%
t	813	10.5%
a	598	7.7%
i	590	7.6%
o	536	6.9%
s	524	6.8%
n	483	6.3%
r	433	5.6%
h	403	5.2%
l	319	4.1%
Other values (16)	2030	26.3%

Other Punctuation

Value	Count	Frequency (%)
,	32	47.1%
'	18	26.5%
?	9	13.2%
"	8	11.8%
;	1	1.5%

Decimal Number

Value	Count	Frequency (%)
0	4	57.1%
1	2	28.6%
2	1	14.3%

Open Punctuation

Value	Count	Frequency (%)
(	7	87.5%
[	1	12.5%

Close Punctuation

Value	Count	Frequency (%)
)	7	87.5%
]	1	12.5%

Space Separator

Value	Count	Frequency (%)
	1556	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	10	100.0%

Uppercase Letter

Value	Count	Frequency (%)
N	4	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	7726	82.3%
Common	1658	17.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	993	12.9%
t	813	10.5%
a	598	7.7%
i	590	7.6%
o	536	6.9%
s	524	6.8%
n	483	6.3%
r	433	5.6%
h	403	5.2%
l	319	4.1%
Other values (17)	2034	26.3%

Common

Value	Count	Frequency (%)
	1556	93.8%
,	32	1.9%
'	18	1.1%
-	10	0.6%
?	9	0.5%
"	8	0.5%
(	7	0.4%
)	7	0.4%
0	4	0.2%
1	2	0.1%
Other values (5)	5	0.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9383	> 99.9%
Punctuation	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1556	16.6%
e	993	10.6%
t	813	8.7%
a	598	6.4%
i	590	6.3%
o	536	5.7%
s	524	5.6%
n	483	5.1%
r	433	4.6%
h	403	4.3%
Other values (31)	2454	26.2%

Punctuation

Value	Count	Frequency (%)
’	1	100.0%

study_3_ethic_acc
Categorical

HIGH CORRELATION
MISSING

Distinct	3
Distinct (%)	1.3%
Missing	270
Missing (%)	54.1%
Memory size	839.0 B

Somewhat acceptable	134
Neutral	56
Somewhat unacceptable	39

Length

Max length	21
Median length	19
Mean length	16.40611354
Min length	7

Characters and Unicode

Total characters	3757
Distinct characters	17
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Neutral
2nd row	Somewhat acceptable
3rd row	Somewhat acceptable
4th row	Neutral
5th row	Neutral

Common Values

Value	Count	Frequency (%)
Somewhat acceptable	134	26.9%
Neutral	56	11.2%
Somewhat unacceptable	39	7.8%
(Missing)	270	54.1%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
somewhat	173	43.0%
acceptable	134	33.3%
neutral	56	13.9%
unacceptable	39	9.7%

Most occurring characters

Value	Count	Frequency (%)
e	575	15.3%
a	575	15.3%
t	402	10.7%
c	346	9.2%
l	229	6.1%
S	173	4.6%
b	173	4.6%
p	173	4.6%
	173	4.6%
o	173	4.6%
Other values (7)	765	20.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	3355	89.3%
Uppercase Letter	229	6.1%
Space Separator	173	4.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	575	17.1%
a	575	17.1%
t	402	12.0%
c	346	10.3%
l	229	6.8%
b	173	5.2%
p	173	5.2%
o	173	5.2%
h	173	5.2%
w	173	5.2%
Other values (4)	363	10.8%

Uppercase Letter

Value	Count	Frequency (%)
S	173	75.5%
N	56	24.5%

Space Separator

Value	Count	Frequency (%)
	173	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	3584	95.4%
Common	173	4.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	575	16.0%
a	575	16.0%
t	402	11.2%
c	346	9.7%
l	229	6.4%
S	173	4.8%
b	173	4.8%
p	173	4.8%
o	173	4.8%
h	173	4.8%
Other values (6)	592	16.5%

Common

Value	Count	Frequency (%)
	173	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	3757	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	575	15.3%
a	575	15.3%
t	402	10.7%
c	346	9.2%
l	229	6.1%
S	173	4.6%
b	173	4.6%
p	173	4.6%
	173	4.6%
o	173	4.6%
Other values (7)	765	20.4%

study_3_conc
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct	227
Distinct (%)	99.6%
Missing	271
Missing (%)	54.3%
Memory size	4.0 KiB

None	2
this is more acceptable because participants are informed ahead of time about the use of their data	1
the study was completely transparent	1
most importantly, the researchers got the approval of their users first at least the users were aware that they were taking part in a study even though, in my mind, they were severely underpaid	1
i feel that since all the participants were informed of the process and offered compensation and they willingly participated, there are not any unethical practices used in the process	1
Other values (222)	222

Length

Max length	719
Median length	152.5
Mean length	126.0175439
Min length	4

Characters and Unicode

Total characters	28732
Distinct characters	48
Distinct categories	10 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	226 ?
Unique (%)	99.1%

Sample

1st row	i find this is ethical as long as participants were fully aware of what was being monitored the results are interesting! no concerns
2nd row	as long as the facebook users were informed that they would be in a study i feel it is fair it was up to the users whether they wanted to participate or not also, they were encouraged, but not actually made to like the facebook study
3rd row	the web extension being used was invasive, even if it was used with consent the people participating in the study are not educated enough on exactly how much information the web extension was taking
4th row	the researchers seem in some ways to try manipulating political viewpoints in a segment of the population for the sake of science
5th row	people willingly consented to being part of the research study, so i believe the study was completely acceptable

Common Values

Value	Count	Frequency (%)
None	2	0.4%
this is more acceptable because participants are informed ahead of time about the use of their data	1	0.2%
the study was completely transparent	1	0.2%
most importantly, the researchers got the approval of their users first at least the users were aware that they were taking part in a study even though, in my mind, they were severely underpaid	1	0.2%
i feel that since all the participants were informed of the process and offered compensation and they willingly participated, there are not any unethical practices used in the process	1	0.2%
my only concern is that the browser extension allowed researches to see all of the users' posts i think this would be okay if it was explicitly consented to by the participants, though the above doesn't specify	1	0.2%
the researchers gave users an option whether they wanted to be a part of the study or not	1	0.2%
this is clearly trying to skew how people view certain topics in social media if thousands of people are not "liking" posts organically it makes post engagement fake especially if this was something that was more related to fake news, and they receive 30k likes, that is problematic	1	0.2%
participants self selected and were informed the option to continue to participate in the research was at their discretion i find no ethical concerns with this methodi am curious about how the researchers determined that the original political views were unchanged even in the face of more exposure to opposing news sources especially since the participants were voluntarily visiting the sites with more frequency and had less negative views about them	1	0.2%
i see no real ethical issue with this statement that is shown above	1	0.2%
Other values (217)	217	43.5%
(Missing)	271	54.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	334	6.6%
to	164	3.3%
i	128	2.5%
of	120	2.4%
they	104	2.1%
were	104	2.1%
and	98	1.9%
a	97	1.9%
that	96	1.9%
study	92	1.8%
Other values (894)	3709	73.5%

Most occurring characters

Value	Count	Frequency (%)
	4835	16.8%
e	3175	11.1%
t	2625	9.1%
i	1851	6.4%
a	1826	6.4%
o	1655	5.8%
s	1582	5.5%
n	1522	5.3%
r	1337	4.7%
h	1211	4.2%
Other values (38)	7113	24.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	23632	82.2%
Space Separator	4835	16.8%
Other Punctuation	214	0.7%
Decimal Number	20	0.1%
Dash Punctuation	10	< 0.1%
Currency Symbol	8	< 0.1%
Open Punctuation	5	< 0.1%
Close Punctuation	5	< 0.1%
Uppercase Letter	2	< 0.1%
Final Punctuation	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	3175	13.4%
t	2625	11.1%
i	1851	7.8%
a	1826	7.7%
o	1655	7.0%
s	1582	6.7%
n	1522	6.4%
r	1337	5.7%
h	1211	5.1%
l	835	3.5%
Other values (16)	6013	25.4%

Other Punctuation

Value	Count	Frequency (%)
,	127	59.3%
'	60	28.0%
"	12	5.6%
?	10	4.7%
!	2	0.9%
%	1	0.5%
;	1	0.5%
:	1	0.5%

Decimal Number

Value	Count	Frequency (%)
0	6	30.0%
5	6	30.0%
8	4	20.0%
3	2	10.0%
1	1	5.0%
2	1	5.0%

Dash Punctuation

Value	Count	Frequency (%)
-	8	80.0%
—	2	20.0%

Space Separator

Value	Count	Frequency (%)
	4835	100.0%

Currency Symbol

Value	Count	Frequency (%)
$	8	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	5	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	5	100.0%

Uppercase Letter

Value	Count	Frequency (%)
N	2	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	23634	82.3%
Common	5098	17.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	3175	13.4%
t	2625	11.1%
i	1851	7.8%
a	1826	7.7%
o	1655	7.0%
s	1582	6.7%
n	1522	6.4%
r	1337	5.7%
h	1211	5.1%
l	835	3.5%
Other values (17)	6015	25.5%

Common

Value	Count	Frequency (%)
	4835	94.8%
,	127	2.5%
'	60	1.2%
"	12	0.2%
?	10	0.2%
$	8	0.2%
-	8	0.2%
0	6	0.1%
5	6	0.1%
(	5	0.1%
Other values (11)	21	0.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	28729	> 99.9%
Punctuation	3	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	4835	16.8%
e	3175	11.1%
t	2625	9.1%
i	1851	6.4%
a	1826	6.4%
o	1655	5.8%
s	1582	5.5%
n	1522	5.3%
r	1337	4.7%
h	1211	4.2%
Other values (36)	7110	24.7%

Punctuation

Value	Count	Frequency (%)
—	2	66.7%
’	1	33.3%

study_3_add_info
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Distinct	76
Distinct (%)	100.0%
Missing	423
Missing (%)	84.8%
Memory size	4.0 KiB

if the researchers just tracked the people that signed up for the study without asking them first	1
i'm not sure who would want the results, but to either take money or get the results most would take the money other survey makers would take the results just because it's free information	1
i think the information is going to be skewed based upon what the user thinks the researcher is looking for they are also more likely to click on political sites because they want to make sure that the researcher is gathering enough data from them	1
what was collected by the extension would be valuable	1
so many ways for people to get manipulated with these studies i agree with this one being one of the good ones	1
Other values (71)	71

Length

Max length	298
Median length	107.5
Mean length	112.7763158
Min length	4

Characters and Unicode

Total characters	8571
Distinct characters	41
Distinct categories	10 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	76 ?
Unique (%)	100.0%

Sample

1st row	making the source code for the web extension publicly available to have complete transparency over what the extension was doing
2nd row	since the study is revealed as a study, i think it’s ethical, but mostly nonsensical
3rd row	if the browser extension tracked anything besides what is stated it would be unacceptable
4th row	glad to hear the participants know about the research they will be a part of & be rewarded for their time
5th row	since they were aware they were taking part in a study it all seems above board i would generally prefer not to have to install anything

Common Values

Value	Count	Frequency (%)
if the researchers just tracked the people that signed up for the study without asking them first	1	0.2%
i'm not sure who would want the results, but to either take money or get the results most would take the money other survey makers would take the results just because it's free information	1	0.2%
i think the information is going to be skewed based upon what the user thinks the researcher is looking for they are also more likely to click on political sites because they want to make sure that the researcher is gathering enough data from them	1	0.2%
what was collected by the extension would be valuable	1	0.2%
so many ways for people to get manipulated with these studies i agree with this one being one of the good ones	1	0.2%
everyone was informed, so i think this is a good study	1	0.2%
i think the level of compensation was woefully low which could potentially have skewed the results based on who would possibly participate for such a small amount of money or some lottery chance(i notice that the word acceptable is misspelled in the completely acceptable option)	1	0.2%
i would just want to be sure that participants are well informed about the data collection process	1	0.2%
i would find it unethical if the researchers mislead the participants or if they used the browser extension to gather information that the participants were not informed of	1	0.2%
data should have been shown in a comparison form	1	0.2%
Other values (66)	66	13.2%
(Missing)	423	84.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	123	8.0%
to	48	3.1%
i	38	2.5%
would	37	2.4%
of	29	1.9%
that	28	1.8%
study	24	1.6%
was	23	1.5%
be	23	1.5%
they	22	1.4%
Other values (458)	1134	74.2%

Most occurring characters

Value	Count	Frequency (%)
	1456	17.0%
e	946	11.0%
t	763	8.9%
a	558	6.5%
o	547	6.4%
i	473	5.5%
s	449	5.2%
n	407	4.7%
r	385	4.5%
h	349	4.1%
Other values (31)	2238	26.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7026	82.0%
Space Separator	1456	17.0%
Other Punctuation	60	0.7%
Decimal Number	10	0.1%
Open Punctuation	5	0.1%
Close Punctuation	5	0.1%
Dash Punctuation	4	< 0.1%
Currency Symbol	3	< 0.1%
Uppercase Letter	1	< 0.1%
Final Punctuation	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	946	13.5%
t	763	10.9%
a	558	7.9%
o	547	7.8%
i	473	6.7%
s	449	6.4%
n	407	5.8%
r	385	5.5%
h	349	5.0%
l	286	4.1%
Other values (15)	1863	26.5%

Other Punctuation

Value	Count	Frequency (%)
,	34	56.7%
'	20	33.3%
?	4	6.7%
&	1	1.7%
!	1	1.7%

Decimal Number

Value	Count	Frequency (%)
5	5	50.0%
8	3	30.0%
1	1	10.0%
0	1	10.0%

Space Separator

Value	Count	Frequency (%)
	1456	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	5	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	5	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	4	100.0%

Currency Symbol

Value	Count	Frequency (%)
$	3	100.0%

Uppercase Letter

Value	Count	Frequency (%)
N	1	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	7027	82.0%
Common	1544	18.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	946	13.5%
t	763	10.9%
a	558	7.9%
o	547	7.8%
i	473	6.7%
s	449	6.4%
n	407	5.8%
r	385	5.5%
h	349	5.0%
l	286	4.1%
Other values (16)	1864	26.5%

Common

Value	Count	Frequency (%)
	1456	94.3%
,	34	2.2%
'	20	1.3%
5	5	0.3%
(	5	0.3%
)	5	0.3%
-	4	0.3%
?	4	0.3%
8	3	0.2%
$	3	0.2%
Other values (5)	5	0.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8570	> 99.9%
Punctuation	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1456	17.0%
e	946	11.0%
t	763	8.9%
a	558	6.5%
o	547	6.4%
i	473	5.5%
s	449	5.2%
n	407	4.7%
r	385	4.5%
h	349	4.1%
Other values (30)	2237	26.1%

Punctuation

Value	Count	Frequency (%)
’	1	100.0%

study_4_ethic_acc
Categorical

HIGH CORRELATION
MISSING

Distinct	3
Distinct (%)	0.9%
Missing	180
Missing (%)	36.1%
Memory size	839.0 B

Somewhat acceptable	121
Somewhat unacceptable	110
Neutral	88

Length

Max length	21
Median length	19
Mean length	16.37931034
Min length	7

Characters and Unicode

Total characters	5225
Distinct characters	17
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Neutral
2nd row	Neutral
3rd row	Somewhat unacceptable
4th row	Somewhat unacceptable
5th row	Somewhat acceptable

Common Values

Value	Count	Frequency (%)
Somewhat acceptable	121	24.2%
Somewhat unacceptable	110	22.0%
Neutral	88	17.6%
(Missing)	180	36.1%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
somewhat	231	42.0%
acceptable	121	22.0%
unacceptable	110	20.0%
neutral	88	16.0%

Most occurring characters

Value	Count	Frequency (%)
e	781	14.9%
a	781	14.9%
t	550	10.5%
c	462	8.8%
l	319	6.1%
S	231	4.4%
b	231	4.4%
p	231	4.4%
	231	4.4%
o	231	4.4%
Other values (7)	1177	22.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	4675	89.5%
Uppercase Letter	319	6.1%
Space Separator	231	4.4%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	781	16.7%
a	781	16.7%
t	550	11.8%
c	462	9.9%
l	319	6.8%
b	231	4.9%
p	231	4.9%
o	231	4.9%
h	231	4.9%
w	231	4.9%
Other values (4)	627	13.4%

Uppercase Letter

Value	Count	Frequency (%)
S	231	72.4%
N	88	27.6%

Space Separator

Value	Count	Frequency (%)
	231	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	4994	95.6%
Common	231	4.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	781	15.6%
a	781	15.6%
t	550	11.0%
c	462	9.3%
l	319	6.4%
S	231	4.6%
b	231	4.6%
p	231	4.6%
o	231	4.6%
h	231	4.6%
Other values (6)	946	18.9%

Common

Value	Count	Frequency (%)
	231	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	5225	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	781	14.9%
a	781	14.9%
t	550	10.5%
c	462	8.8%
l	319	6.1%
S	231	4.4%
b	231	4.4%
p	231	4.4%
	231	4.4%
o	231	4.4%
Other values (7)	1177	22.5%

study_4_conc
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct	253
Distinct (%)	100.0%
Missing	246
Missing (%)	49.3%
Memory size	4.0 KiB

same, the researchers created fake accounts that looked like people this is fraudulent	1
i don't agree with them hiding behind bots to complete their research and not telling the end user this was for research purposes upfront	1
the idea that one is not informed that their replies are being used is not acceptable to me, because information in many twitter accounts can successfully identify the person i use twitter a lot, only for sports but i do see a lot of different topics, many political that are posted the amount of bots on twitter is astounding to me, and i would never consider using it for data processing just my opinion	1
i'm not a big fan of participants being unaware, but as long as they remained anonymous i'm neutral on this one	1
i object to most studies in which users are not informed that they are being studied and that they are being manipulatedsecondly, i would like to see the type of reply that was originally sent this study almost directly contradicts the results reached in the last study in which people deleted their hate speech because they got a link to a fact checking site along with an empathetic responselastly, i now do my own fact checking after learning that quite a few of these fact checkers are deliberately manipulating and distorting info because of their own bias i simply no longer trust the "fact checkers"	1
Other values (248)	248

Length

Max length	815
Median length	202
Mean length	140.0079051
Min length	4

Characters and Unicode

Total characters	35422
Distinct characters	53
Distinct categories	12 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	253 ?
Unique (%)	100.0%

Sample

1st row	i am uncertain how i feel completely about a researcher creating a fake account however i do understand the desire to protect themselves and to not give away their actions as being part of a study this misinformation needed to be corrected for the public but it opened the original poster to toxicity the op may not have known it was incorrect
2nd row	users were not aware of what was going on so they were possibly more honest in their opinions because they had no idea they were being analyzed
3rd row	many of the people that have large political followings on twitter (and many who don't) often know already the news they are sharing is fake it's political partisanship and the spreading of propaganda some might post fake news only to gain more followers (the masses) if they believe it serves that end
4th row	this study is unethfull disclosure of intent of researchical because they were not informed of the research study
5th row	i have the same objections as before, it’s deceitful and lazy and twitter is a non representative sample of the public

Common Values

Value	Count	Frequency (%)
same, the researchers created fake accounts that looked like people this is fraudulent	1	0.2%
i don't agree with them hiding behind bots to complete their research and not telling the end user this was for research purposes upfront	1	0.2%
the idea that one is not informed that their replies are being used is not acceptable to me, because information in many twitter accounts can successfully identify the person i use twitter a lot, only for sports but i do see a lot of different topics, many political that are posted the amount of bots on twitter is astounding to me, and i would never consider using it for data processing just my opinion	1	0.2%
i'm not a big fan of participants being unaware, but as long as they remained anonymous i'm neutral on this one	1	0.2%
i object to most studies in which users are not informed that they are being studied and that they are being manipulatedsecondly, i would like to see the type of reply that was originally sent this study almost directly contradicts the results reached in the last study in which people deleted their hate speech because they got a link to a fact checking site along with an empathetic responselastly, i now do my own fact checking after learning that quite a few of these fact checkers are deliberately manipulating and distorting info because of their own bias i simply no longer trust the "fact checkers"	1	0.2%
since this was done in a public setting, i feel like it is more ethically acceptable than the other study that was similar, but done through private messages	1	0.2%
i personally have no issue with this study, though objectively it seems a little dubious to study individuals without their knowledge like this	1	0.2%
this is all public, so i have no issue with the researchers observing this	1	0.2%
i feel like this is acceptable because when you sign up for social media, if you are posting something publicly it is assumed that anyone can look at these posts and reply to them	1	0.2%
i have found that some "fact-checking" sites end up having wrong information as well a great case is how the idea that masks don't work spread many studies have been conducted and some very reputable scientific organizations have studies on their websites that say masks do not work, but the data is typically very small or the type of mask used was a very thin cloth mask, but it gives fuel to people who spread the false information that masking does not work	1	0.2%
Other values (243)	243	48.7%
(Missing)	246	49.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	302	4.9%
to	152	2.5%
i	152	2.5%
of	147	2.4%
that	147	2.4%
they	137	2.2%
a	135	2.2%
is	120	1.9%
and	111	1.8%
not	110	1.8%
Other values (1090)	4690	75.6%

Most occurring characters

Value	Count	Frequency (%)
	5976	16.9%
e	3602	10.2%
t	3227	9.1%
a	2341	6.6%
i	2331	6.6%
o	2035	5.7%
n	2025	5.7%
s	1865	5.3%
r	1535	4.3%
h	1493	4.2%
Other values (43)	8992	25.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	28986	81.8%
Space Separator	5976	16.9%
Other Punctuation	356	1.0%
Dash Punctuation	34	0.1%
Decimal Number	34	0.1%
Open Punctuation	10	< 0.1%
Close Punctuation	9	< 0.1%
Final Punctuation	6	< 0.1%
Math Symbol	5	< 0.1%
Connector Punctuation	4	< 0.1%
Other values (2)	2	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	3602	12.4%
t	3227	11.1%
a	2341	8.1%
i	2331	8.0%
o	2035	7.0%
n	2025	7.0%
s	1865	6.4%
r	1535	5.3%
h	1493	5.2%
d	1012	3.5%
Other values (16)	7520	25.9%

Other Punctuation

Value	Count	Frequency (%)
,	176	49.4%
'	105	29.5%
"	46	12.9%
?	13	3.7%
%	9	2.5%
&	3	0.8%
:	2	0.6%
!	1	0.3%
;	1	0.3%

Decimal Number

Value	Count	Frequency (%)
2	13	38.2%
0	12	35.3%
7	2	5.9%
3	2	5.9%
5	2	5.9%
1	1	2.9%
4	1	2.9%
9	1	2.9%

Math Symbol

Value	Count	Frequency (%)
=	4	80.0%
~	1	20.0%

Space Separator

Value	Count	Frequency (%)
	5976	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	34	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	10	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	9	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	6	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	4	100.0%

Uppercase Letter

Value	Count	Frequency (%)
N	1	100.0%

Initial Punctuation

Value	Count	Frequency (%)
‘	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	28987	81.8%
Common	6435	18.2%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	3602	12.4%
t	3227	11.1%
a	2341	8.1%
i	2331	8.0%
o	2035	7.0%
n	2025	7.0%
s	1865	6.4%
r	1535	5.3%
h	1493	5.2%
d	1012	3.5%
Other values (17)	7521	25.9%

Common

Value	Count	Frequency (%)
	5976	92.9%
,	176	2.7%
'	105	1.6%
"	46	0.7%
-	34	0.5%
?	13	0.2%
2	13	0.2%
0	12	0.2%
(	10	0.2%
)	9	0.1%
Other values (16)	41	0.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	35415	> 99.9%
Punctuation	7	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	5976	16.9%
e	3602	10.2%
t	3227	9.1%
a	2341	6.6%
i	2331	6.6%
o	2035	5.7%
n	2025	5.7%
s	1865	5.3%
r	1535	4.3%
h	1493	4.2%
Other values (41)	8985	25.4%

Punctuation

Value	Count	Frequency (%)
’	6	85.7%
‘	1	14.3%

study_4_add_info
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Distinct	87
Distinct (%)	100.0%
Missing	412
Missing (%)	82.6%
Memory size	4.0 KiB

the researchers had a purpose in seeing the responses of those interacting with the post i do not agree with how it was done entirely however i do not know a better way to get the results that were desired	1
i would not think it is ethical no matter what	1
the researchers relied on "fact checkers" to determine if the information was "fake" or not if there existed a bias or margin of error in the fact checker's process then the researchers would be working with wrong information themselves	1
i find it acceptable because this method is helping to slow the spread of misinformation	1
the outcome of this study has also resulted in negative behavior by the participants due to the experiment	1
Other values (82)	82

Length

Max length	577
Median length	118
Mean length	113.908046
Min length	32

Characters and Unicode

Total characters	9910
Distinct characters	37
Distinct categories	7 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	87 ?
Unique (%)	100.0%

Sample

1st row	the researchers had a purpose in seeing the responses of those interacting with the post i do not agree with how it was done entirely however i do not know a better way to get the results that were desired
2nd row	full disclosure of intent of researchers
3rd row	please see comments from the first study
4th row	if they had told people they would be part of an experiment
5th row	i don't use twitter too many bots

Common Values

Value	Count	Frequency (%)
the researchers had a purpose in seeing the responses of those interacting with the post i do not agree with how it was done entirely however i do not know a better way to get the results that were desired	1	0.2%
i would not think it is ethical no matter what	1	0.2%
the researchers relied on "fact checkers" to determine if the information was "fake" or not if there existed a bias or margin of error in the fact checker's process then the researchers would be working with wrong information themselves	1	0.2%
i find it acceptable because this method is helping to slow the spread of misinformation	1	0.2%
the outcome of this study has also resulted in negative behavior by the participants due to the experiment	1	0.2%
completely unethical customers were unaware of the study and fake accounts were made no one was compensated or aware	1	0.2%
i would like to know example content of the tweets and be shown an example of the bot accounts - it's hard to know exactly how individuals would react to someone pointing out their wrong without knowing the profile of the person pointing out the error	1	0.2%
researchers should not be spreading fake news in the name of research	1	0.2%
fake news is a plague but there's got to be better ways to get rid of it deception	1	0.2%
i don't think it's acceptable because non human bots were used and even though they linked to a fact checking website, they are still influencing people and that will cause more divisiveness	1	0.2%
Other values (77)	77	15.4%
(Missing)	412	82.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	98	5.5%
to	59	3.3%
i	41	2.3%
of	37	2.1%
it	36	2.0%
a	33	1.8%
if	32	1.8%
they	31	1.7%
and	29	1.6%
that	29	1.6%
Other values (528)	1360	76.2%

Most occurring characters

Value	Count	Frequency (%)
	1703	17.2%
e	1088	11.0%
t	910	9.2%
a	606	6.1%
o	572	5.8%
i	571	5.8%
s	515	5.2%
n	483	4.9%
h	441	4.5%
r	438	4.4%
Other values (27)	2583	26.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	8109	81.8%
Space Separator	1703	17.2%
Other Punctuation	76	0.8%
Dash Punctuation	10	0.1%
Open Punctuation	4	< 0.1%
Close Punctuation	4	< 0.1%
Decimal Number	4	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1088	13.4%
t	910	11.2%
a	606	7.5%
o	572	7.1%
i	571	7.0%
s	515	6.4%
n	483	6.0%
h	441	5.4%
r	438	5.4%
l	309	3.8%
Other values (16)	2176	26.8%

Other Punctuation

Value	Count	Frequency (%)
,	29	38.2%
'	25	32.9%
"	14	18.4%
?	6	7.9%
;	2	2.6%

Decimal Number

Value	Count	Frequency (%)
0	3	75.0%
2	1	25.0%

Space Separator

Value	Count	Frequency (%)
	1703	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	10	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	4	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	4	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8109	81.8%
Common	1801	18.2%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1088	13.4%
t	910	11.2%
a	606	7.5%
o	572	7.1%
i	571	7.0%
s	515	6.4%
n	483	6.0%
h	441	5.4%
r	438	5.4%
l	309	3.8%
Other values (16)	2176	26.8%

Common

Value	Count	Frequency (%)
	1703	94.6%
,	29	1.6%
'	25	1.4%
"	14	0.8%
-	10	0.6%
?	6	0.3%
(	4	0.2%
)	4	0.2%
0	3	0.2%
;	2	0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9910	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1703	17.2%
e	1088	11.0%
t	910	9.2%
a	606	6.1%
o	572	5.8%
i	571	5.8%
s	515	5.2%
n	483	4.9%
h	441	4.5%
r	438	4.4%
Other values (27)	2583	26.1%

design_cont
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	839.0 B

Very important	165
Moderately important	124
Extremely important	105
Slightly important	67
Not at all important	38

Length

Max length	20
Median length	19
Mean length	17.53707415
Min length	14

Characters and Unicode

Total characters	8751
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Not at all important
2nd row	Not at all important
3rd row	Extremely important
4th row	Moderately important
5th row	Extremely important

Common Values

Value	Count	Frequency (%)
Very important	165	33.1%
Moderately important	124	24.8%
Extremely important	105	21.0%
Slightly important	67	13.4%
Not at all important	38	7.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	46.5%
very	165	15.4%
moderately	124	11.5%
extremely	105	9.8%
slightly	67	6.2%
not	38	3.5%
at	38	3.5%
all	38	3.5%

Most occurring characters

Value	Count	Frequency (%)
t	1370	15.7%
r	893	10.2%
a	699	8.0%
o	661	7.6%
e	623	7.1%
m	604	6.9%
	575	6.6%
i	566	6.5%
p	499	5.7%
n	499	5.7%
Other values (11)	1762	20.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7677	87.7%
Space Separator	575	6.6%
Uppercase Letter	499	5.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1370	17.8%
r	893	11.6%
a	699	9.1%
o	661	8.6%
e	623	8.1%
m	604	7.9%
i	566	7.4%
p	499	6.5%
n	499	6.5%
y	461	6.0%
Other values (5)	802	10.4%

Uppercase Letter

Value	Count	Frequency (%)
V	165	33.1%
M	124	24.8%
E	105	21.0%
S	67	13.4%
N	38	7.6%

Space Separator

Value	Count	Frequency (%)
	575	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8176	93.4%
Common	575	6.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1370	16.8%
r	893	10.9%
a	699	8.5%
o	661	8.1%
e	623	7.6%
m	604	7.4%
i	566	6.9%
p	499	6.1%
n	499	6.1%
y	461	5.6%
Other values (10)	1301	15.9%

Common

Value	Count	Frequency (%)
	575	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8751	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1370	15.7%
r	893	10.2%
a	699	8.0%
o	661	7.6%
e	623	7.1%
m	604	6.9%
	575	6.6%
i	566	6.5%
p	499	5.7%
n	499	5.7%
Other values (11)	1762	20.1%

design_num_users
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	839.0 B

Not at all important	125
Very important	106
Slightly important	95
Moderately important	89
Extremely important	84

Length

Max length	20
Median length	19
Mean length	18.17635271
Min length	14

Characters and Unicode

Total characters	9070
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Not at all important
2nd row	Not at all important
3rd row	Very important
4th row	Moderately important
5th row	Not at all important

Common Values

Value	Count	Frequency (%)
Not at all important	125	25.1%
Very important	106	21.2%
Slightly important	95	19.0%
Moderately important	89	17.8%
Extremely important	84	16.8%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	40.0%
not	125	10.0%
at	125	10.0%
all	125	10.0%
very	106	8.5%
slightly	95	7.6%
moderately	89	7.1%
extremely	84	6.7%

Most occurring characters

Value	Count	Frequency (%)
t	1516	16.7%
a	838	9.2%
r	778	8.6%
	749	8.3%
o	713	7.9%
l	613	6.8%
i	594	6.5%
m	583	6.4%
n	499	5.5%
p	499	5.5%
Other values (11)	1688	18.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7822	86.2%
Space Separator	749	8.3%
Uppercase Letter	499	5.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1516	19.4%
a	838	10.7%
r	778	9.9%
o	713	9.1%
l	613	7.8%
i	594	7.6%
m	583	7.5%
n	499	6.4%
p	499	6.4%
e	452	5.8%
Other values (5)	737	9.4%

Uppercase Letter

Value	Count	Frequency (%)
N	125	25.1%
V	106	21.2%
S	95	19.0%
M	89	17.8%
E	84	16.8%

Space Separator

Value	Count	Frequency (%)
	749	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8321	91.7%
Common	749	8.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1516	18.2%
a	838	10.1%
r	778	9.3%
o	713	8.6%
l	613	7.4%
i	594	7.1%
m	583	7.0%
n	499	6.0%
p	499	6.0%
e	452	5.4%
Other values (10)	1236	14.9%

Common

Value	Count	Frequency (%)
	749	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9070	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1516	16.7%
a	838	9.2%
r	778	8.6%
	749	8.3%
o	713	7.9%
l	613	6.8%
i	594	6.5%
m	583	6.4%
n	499	5.5%
p	499	5.5%
Other values (11)	1688	18.6%

design_res_purp
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	839.0 B

Very important	136
Extremely important	121
Moderately important	109
Slightly important	68
Not at all important	65

Length

Max length	20
Median length	19
Mean length	17.8496994
Min length	14

Characters and Unicode

Total characters	8907
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Not at all important
2nd row	Not at all important
3rd row	Very important
4th row	Extremely important
5th row	Not at all important

Common Values

Value	Count	Frequency (%)
Very important	136	27.3%
Extremely important	121	24.2%
Moderately important	109	21.8%
Slightly important	68	13.6%
Not at all important	65	13.0%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	44.2%
very	136	12.1%
extremely	121	10.7%
moderately	109	9.7%
slightly	68	6.0%
not	65	5.8%
at	65	5.8%
all	65	5.8%

Most occurring characters

Value	Count	Frequency (%)
t	1426	16.0%
r	865	9.7%
a	738	8.3%
o	673	7.6%
	629	7.1%
m	620	7.0%
e	596	6.7%
i	567	6.4%
p	499	5.6%
n	499	5.6%
Other values (11)	1795	20.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7779	87.3%
Space Separator	629	7.1%
Uppercase Letter	499	5.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1426	18.3%
r	865	11.1%
a	738	9.5%
o	673	8.7%
m	620	8.0%
e	596	7.7%
i	567	7.3%
p	499	6.4%
n	499	6.4%
l	496	6.4%
Other values (5)	800	10.3%

Uppercase Letter

Value	Count	Frequency (%)
V	136	27.3%
E	121	24.2%
M	109	21.8%
S	68	13.6%
N	65	13.0%

Space Separator

Value	Count	Frequency (%)
	629	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8278	92.9%
Common	629	7.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1426	17.2%
r	865	10.4%
a	738	8.9%
o	673	8.1%
m	620	7.5%
e	596	7.2%
i	567	6.8%
p	499	6.0%
n	499	6.0%
l	496	6.0%
Other values (10)	1299	15.7%

Common

Value	Count	Frequency (%)
	629	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8907	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1426	16.0%
r	865	9.7%
a	738	8.3%
o	673	7.6%
	629	7.1%
m	620	7.0%
e	596	6.7%
i	567	6.4%
p	499	5.6%
n	499	5.6%
Other values (11)	1795	20.2%

design_len_data
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	839.0 B

Very important	120
Moderately important	119
Slightly important	107
Not at all important	97
Extremely important	56

Length

Max length	20
Median length	19
Mean length	18.01603206
Min length	14

Characters and Unicode

Total characters	8990
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Not at all important
2nd row	Not at all important
3rd row	Extremely important
4th row	Very important
5th row	Not at all important

Common Values

Value	Count	Frequency (%)
Very important	120	24.0%
Moderately important	119	23.8%
Slightly important	107	21.4%
Not at all important	97	19.4%
Extremely important	56	11.2%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	41.9%
very	120	10.1%
moderately	119	10.0%
slightly	107	9.0%
not	97	8.1%
at	97	8.1%
all	97	8.1%
extremely	56	4.7%

Most occurring characters

Value	Count	Frequency (%)
t	1474	16.4%
a	812	9.0%
r	794	8.8%
o	715	8.0%
	693	7.7%
i	606	6.7%
l	583	6.5%
m	555	6.2%
p	499	5.6%
n	499	5.6%
Other values (11)	1760	19.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7798	86.7%
Space Separator	693	7.7%
Uppercase Letter	499	5.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1474	18.9%
a	812	10.4%
r	794	10.2%
o	715	9.2%
i	606	7.8%
l	583	7.5%
m	555	7.1%
p	499	6.4%
n	499	6.4%
e	470	6.0%
Other values (5)	791	10.1%

Uppercase Letter

Value	Count	Frequency (%)
V	120	24.0%
M	119	23.8%
S	107	21.4%
N	97	19.4%
E	56	11.2%

Space Separator

Value	Count	Frequency (%)
	693	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8297	92.3%
Common	693	7.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1474	17.8%
a	812	9.8%
r	794	9.6%
o	715	8.6%
i	606	7.3%
l	583	7.0%
m	555	6.7%
p	499	6.0%
n	499	6.0%
e	470	5.7%
Other values (10)	1290	15.5%

Common

Value	Count	Frequency (%)
	693	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8990	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1474	16.4%
a	812	9.0%
r	794	8.8%
o	715	8.0%
	693	7.7%
i	606	6.7%
l	583	6.5%
m	555	6.2%
p	499	5.6%
n	499	5.6%
Other values (11)	1760	19.6%

design_admin_inter
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	839.0 B

Moderately important	139
Very important	109
Slightly important	96
Not at all important	91
Extremely important	64

Length

Max length	20
Median length	19
Mean length	18.17635271
Min length	14

Characters and Unicode

Total characters	9070
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Not at all important
2nd row	Not at all important
3rd row	Moderately important
4th row	Moderately important
5th row	Not at all important

Common Values

Value	Count	Frequency (%)
Moderately important	139	27.9%
Very important	109	21.8%
Slightly important	96	19.2%
Not at all important	91	18.2%
Extremely important	64	12.8%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	42.3%
moderately	139	11.8%
very	109	9.2%
slightly	96	8.1%
not	91	7.7%
at	91	7.7%
all	91	7.7%
extremely	64	5.4%

Most occurring characters

Value	Count	Frequency (%)
t	1479	16.3%
a	820	9.0%
r	811	8.9%
o	729	8.0%
	681	7.5%
i	595	6.6%
l	577	6.4%
m	563	6.2%
e	515	5.7%
p	499	5.5%
Other values (11)	1801	19.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7890	87.0%
Space Separator	681	7.5%
Uppercase Letter	499	5.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1479	18.7%
a	820	10.4%
r	811	10.3%
o	729	9.2%
i	595	7.5%
l	577	7.3%
m	563	7.1%
e	515	6.5%
p	499	6.3%
n	499	6.3%
Other values (5)	803	10.2%

Uppercase Letter

Value	Count	Frequency (%)
M	139	27.9%
V	109	21.8%
S	96	19.2%
N	91	18.2%
E	64	12.8%

Space Separator

Value	Count	Frequency (%)
	681	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8389	92.5%
Common	681	7.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1479	17.6%
a	820	9.8%
r	811	9.7%
o	729	8.7%
i	595	7.1%
l	577	6.9%
m	563	6.7%
e	515	6.1%
p	499	5.9%
n	499	5.9%
Other values (10)	1302	15.5%

Common

Value	Count	Frequency (%)
	681	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9070	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1479	16.3%
a	820	9.0%
r	811	8.9%
o	729	8.0%
	681	7.5%
i	595	6.6%
l	577	6.4%
m	563	6.2%
e	515	5.7%
p	499	5.5%
Other values (11)	1801	19.9%

design_inter_type
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	839.0 B

Very important	162
Moderately important	145
Extremely important	94
Slightly important	65
Not at all important	33

Length

Max length	20
Median length	19
Mean length	17.60320641
Min length	14

Characters and Unicode

Total characters	8784
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Slightly important
2nd row	Not at all important
3rd row	Very important
4th row	Very important
5th row	Extremely important

Common Values

Value	Count	Frequency (%)
Very important	162	32.5%
Moderately important	145	29.1%
Extremely important	94	18.8%
Slightly important	65	13.0%
Not at all important	33	6.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	46.9%
very	162	15.2%
moderately	145	13.6%
extremely	94	8.8%
slightly	65	6.1%
not	33	3.1%
at	33	3.1%
all	33	3.1%

Most occurring characters

Value	Count	Frequency (%)
t	1368	15.6%
r	900	10.2%
a	710	8.1%
o	677	7.7%
e	640	7.3%
m	593	6.8%
	565	6.4%
i	564	6.4%
p	499	5.7%
n	499	5.7%
Other values (11)	1769	20.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7720	87.9%
Space Separator	565	6.4%
Uppercase Letter	499	5.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1368	17.7%
r	900	11.7%
a	710	9.2%
o	677	8.8%
e	640	8.3%
m	593	7.7%
i	564	7.3%
p	499	6.5%
n	499	6.5%
y	466	6.0%
Other values (5)	804	10.4%

Uppercase Letter

Value	Count	Frequency (%)
V	162	32.5%
M	145	29.1%
E	94	18.8%
S	65	13.0%
N	33	6.6%

Space Separator

Value	Count	Frequency (%)
	565	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8219	93.6%
Common	565	6.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1368	16.6%
r	900	11.0%
a	710	8.6%
o	677	8.2%
e	640	7.8%
m	593	7.2%
i	564	6.9%
p	499	6.1%
n	499	6.1%
y	466	5.7%
Other values (10)	1303	15.9%

Common

Value	Count	Frequency (%)
	565	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8784	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1368	15.6%
r	900	10.2%
a	710	8.1%
o	677	7.7%
e	640	7.3%
m	593	6.8%
	565	6.4%
i	564	6.4%
p	499	5.7%
n	499	5.7%
Other values (11)	1769	20.1%

design_partic_aware
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	839.0 B

Extremely important	191
Very important	132
Moderately important	92
Slightly important	53
Not at all important	31

Length

Max length	20
Median length	19
Mean length	17.81763527
Min length	14

Characters and Unicode

Total characters	8891
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Slightly important
2nd row	Moderately important
3rd row	Moderately important
4th row	Extremely important
5th row	Slightly important

Common Values

Value	Count	Frequency (%)
Extremely important	191	38.3%
Very important	132	26.5%
Moderately important	92	18.4%
Slightly important	53	10.6%
Not at all important	31	6.2%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	47.1%
extremely	191	18.0%
very	132	12.5%
moderately	92	8.7%
slightly	53	5.0%
not	31	2.9%
at	31	2.9%
all	31	2.9%

Most occurring characters

Value	Count	Frequency (%)
t	1396	15.7%
r	914	10.3%
e	698	7.9%
m	690	7.8%
a	653	7.3%
o	622	7.0%
	561	6.3%
i	552	6.2%
p	499	5.6%
n	499	5.6%
Other values (11)	1807	20.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7831	88.1%
Space Separator	561	6.3%
Uppercase Letter	499	5.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1396	17.8%
r	914	11.7%
e	698	8.9%
m	690	8.8%
a	653	8.3%
o	622	7.9%
i	552	7.0%
p	499	6.4%
n	499	6.4%
y	468	6.0%
Other values (5)	840	10.7%

Uppercase Letter

Value	Count	Frequency (%)
E	191	38.3%
V	132	26.5%
M	92	18.4%
S	53	10.6%
N	31	6.2%

Space Separator

Value	Count	Frequency (%)
	561	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8330	93.7%
Common	561	6.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1396	16.8%
r	914	11.0%
e	698	8.4%
m	690	8.3%
a	653	7.8%
o	622	7.5%
i	552	6.6%
p	499	6.0%
n	499	6.0%
y	468	5.6%
Other values (10)	1339	16.1%

Common

Value	Count	Frequency (%)
	561	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8891	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1396	15.7%
r	914	10.3%
e	698	7.9%
m	690	7.8%
a	653	7.3%
o	622	7.0%
	561	6.3%
i	552	6.2%
p	499	5.6%
n	499	5.6%
Other values (11)	1807	20.3%

design_inter_impact
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	839.0 B

Very important	139
Moderately important	114
Extremely important	107
Slightly important	80
Not at all important	59

Length

Max length	20
Median length	19
Mean length	17.79358717
Min length	14

Characters and Unicode

Total characters	8879
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Not at all important
2nd row	Not at all important
3rd row	Extremely important
4th row	Very important
5th row	Not at all important

Common Values

Value	Count	Frequency (%)
Very important	139	27.9%
Moderately important	114	22.8%
Extremely important	107	21.4%
Slightly important	80	16.0%
Not at all important	59	11.8%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	44.7%
very	139	12.5%
moderately	114	10.2%
extremely	107	9.6%
slightly	80	7.2%
not	59	5.3%
at	59	5.3%
all	59	5.3%

Most occurring characters

Value	Count	Frequency (%)
t	1417	16.0%
r	859	9.7%
a	731	8.2%
o	672	7.6%
	617	6.9%
m	606	6.8%
e	581	6.5%
i	579	6.5%
l	499	5.6%
p	499	5.6%
Other values (11)	1819	20.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7763	87.4%
Space Separator	617	6.9%
Uppercase Letter	499	5.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1417	18.3%
r	859	11.1%
a	731	9.4%
o	672	8.7%
m	606	7.8%
e	581	7.5%
i	579	7.5%
l	499	6.4%
p	499	6.4%
n	499	6.4%
Other values (5)	821	10.6%

Uppercase Letter

Value	Count	Frequency (%)
V	139	27.9%
M	114	22.8%
E	107	21.4%
S	80	16.0%
N	59	11.8%

Space Separator

Value	Count	Frequency (%)
	617	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8262	93.1%
Common	617	6.9%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1417	17.2%
r	859	10.4%
a	731	8.8%
o	672	8.1%
m	606	7.3%
e	581	7.0%
i	579	7.0%
l	499	6.0%
p	499	6.0%
n	499	6.0%
Other values (10)	1320	16.0%

Common

Value	Count	Frequency (%)
	617	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8879	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1417	16.0%
r	859	9.7%
a	731	8.2%
o	672	7.6%
	617	6.9%
m	606	6.8%
e	581	6.5%
i	579	6.5%
l	499	5.6%
p	499	5.6%
Other values (11)	1819	20.5%

design_type_data
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	839.0 B

Very important	144
Moderately important	130
Extremely important	106
Slightly important	70
Not at all important	49

Length

Max length	20
Median length	19
Mean length	17.7755511
Min length	14

Characters and Unicode

Total characters	8870
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Not at all important
2nd row	Not at all important
3rd row	Not at all important
4th row	Very important
5th row	Extremely important

Common Values

Value	Count	Frequency (%)
Very important	144	28.9%
Moderately important	130	26.1%
Extremely important	106	21.2%
Slightly important	70	14.0%
Not at all important	49	9.8%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	499	45.5%
very	144	13.1%
moderately	130	11.9%
extremely	106	9.7%
slightly	70	6.4%
not	49	4.5%
at	49	4.5%
all	49	4.5%

Most occurring characters

Value	Count	Frequency (%)
t	1402	15.8%
r	879	9.9%
a	727	8.2%
o	678	7.6%
e	616	6.9%
m	605	6.8%
	597	6.7%
i	569	6.4%
p	499	5.6%
n	499	5.6%
Other values (11)	1799	20.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7774	87.6%
Space Separator	597	6.7%
Uppercase Letter	499	5.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	1402	18.0%
r	879	11.3%
a	727	9.4%
o	678	8.7%
e	616	7.9%
m	605	7.8%
i	569	7.3%
p	499	6.4%
n	499	6.4%
l	474	6.1%
Other values (5)	826	10.6%

Uppercase Letter

Value	Count	Frequency (%)
V	144	28.9%
M	130	26.1%
E	106	21.2%
S	70	14.0%
N	49	9.8%

Space Separator

Value	Count	Frequency (%)
	597	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8273	93.3%
Common	597	6.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	1402	16.9%
r	879	10.6%
a	727	8.8%
o	678	8.2%
e	616	7.4%
m	605	7.3%
i	569	6.9%
p	499	6.0%
n	499	6.0%
l	474	5.7%
Other values (10)	1325	16.0%

Common

Value	Count	Frequency (%)
	597	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8870	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	1402	15.8%
r	879	9.9%
a	727	8.2%
o	678	7.6%
e	616	6.9%
m	605	6.8%
	597	6.7%
i	569	6.4%
p	499	5.6%
n	499	5.6%
Other values (11)	1799	20.3%

design_add_fac
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct	192
Distinct (%)	96.0%
Missing	299
Missing (%)	59.9%
Memory size	4.0 KiB

None	9
whether or not the research is going to be publicly available	1
no i think that covered them all	1
awareness by the user, extremely important	1
all the responses were raw and that's how the most accurate data is collected	1
Other values (187)	187

Length

Max length	652
Median length	179.5
Mean length	125.945
Min length	4

Characters and Unicode

Total characters	25189
Distinct characters	43
Distinct categories	10 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	191 ?
Unique (%)	95.5%

Sample

1st row	the only aspects of social media research that would cause concern for me is saving photographs or imaging data
2nd row	none that i can think of, other than what has been asked already
3rd row	reducing any type of hate is always a good thing
4th row	na i have already voiced my concerns about researching this in general from this surveys other questions
5th row	the possibility of bot accounts spreading misinformation or hate speech just for the purpose of an experiment

Common Values

Value	Count	Frequency (%)
None	9	1.8%
whether or not the research is going to be publicly available	1	0.2%
no i think that covered them all	1	0.2%
awareness by the user, extremely important	1	0.2%
all the responses were raw and that's how the most accurate data is collected	1	0.2%
engagement in the study just like this survey, are they participants paying attention	1	0.2%
there are no other aspects that i can think of	1	0.2%
this type of research is only going to give you data on a small demographic as longas the researchers know this	1	0.2%
i cannot think of any other aspects of research conducted on social media that are important to me in determining levels of concern	1	0.2%
once again my focus, based on my experience on social media along with my interest in doing studiessurveys on prolific, is that users be informed if there is an intent to try to change or manipulate someone's online behaviorstudying posts objectively is one thing, but deliberately trying to get someone to act differently is unacceptable that is what advertisers, political operatives and propagandists do it is may also be why i have seen user activity drop off during this current campaign season too many accounts are being flagged for supposed violations when people have only posted honest questions regarding the facts or truth behind some claim	1	0.2%
Other values (182)	182	36.5%
(Missing)	299	59.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	200	4.5%
of	129	2.9%
is	128	2.9%
to	121	2.7%
i	113	2.6%
a	81	1.8%
that	80	1.8%
and	79	1.8%
it	64	1.4%
are	62	1.4%
Other values (1009)	3369	76.1%

Most occurring characters

Value	Count	Frequency (%)
	4253	16.9%
e	2532	10.1%
t	2029	8.1%
a	1715	6.8%
i	1692	6.7%
o	1566	6.2%
n	1479	5.9%
s	1342	5.3%
r	1224	4.9%
h	1051	4.2%
Other values (33)	6306	25.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	20659	82.0%
Space Separator	4253	16.9%
Other Punctuation	229	0.9%
Dash Punctuation	19	0.1%
Uppercase Letter	9	< 0.1%
Open Punctuation	6	< 0.1%
Close Punctuation	5	< 0.1%
Final Punctuation	4	< 0.1%
Decimal Number	4	< 0.1%
Math Symbol	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	2532	12.3%
t	2029	9.8%
a	1715	8.3%
i	1692	8.2%
o	1566	7.6%
n	1479	7.2%
s	1342	6.5%
r	1224	5.9%
h	1051	5.1%
l	794	3.8%
Other values (16)	5235	25.3%

Other Punctuation

Value	Count	Frequency (%)
,	125	54.6%
'	78	34.1%
?	14	6.1%
"	10	4.4%
…	1	0.4%
:	1	0.4%

Decimal Number

Value	Count	Frequency (%)
2	1	25.0%
4	1	25.0%
1	1	25.0%
3	1	25.0%

Space Separator

Value	Count	Frequency (%)
	4253	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	19	100.0%

Uppercase Letter

Value	Count	Frequency (%)
N	9	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	6	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	5	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	4	100.0%

Math Symbol

Value	Count	Frequency (%)
+	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	20668	82.1%
Common	4521	17.9%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	2532	12.3%
t	2029	9.8%
a	1715	8.3%
i	1692	8.2%
o	1566	7.6%
n	1479	7.2%
s	1342	6.5%
r	1224	5.9%
h	1051	5.1%
l	794	3.8%
Other values (17)	5244	25.4%

Common

Value	Count	Frequency (%)
	4253	94.1%
,	125	2.8%
'	78	1.7%
-	19	0.4%
?	14	0.3%
"	10	0.2%
(	6	0.1%
)	5	0.1%
’	4	0.1%
…	1	< 0.1%
Other values (6)	6	0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	25184	> 99.9%
Punctuation	5	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	4253	16.9%
e	2532	10.1%
t	2029	8.1%
a	1715	6.8%
i	1692	6.7%
o	1566	6.2%
n	1479	5.9%
s	1342	5.3%
r	1224	4.9%
h	1051	4.2%
Other values (31)	6301	25.0%

Punctuation

Value	Count	Frequency (%)
’	4	80.0%
…	1	20.0%

rank_sci_repro
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	7
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5.521042084

Minimum	1
Maximum	7
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	2
Q1	5
median	6
Q3	7
95-th percentile	7
Maximum	7
Range	6
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.679386636
Coefficient of variation (CV)	0.3041792855
Kurtosis	0.1087548301
Mean	5.521042084
Median Absolute Deviation (MAD)	1
Skewness	-1.025007039
Sum	2755
Variance	2.820339474
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
7	203	40.7%
6	102	20.4%
5	70	14.0%
4	49	9.8%
3	42	8.4%
2	17	3.4%
1	16	3.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	16	3.2%
2	17	3.4%
3	42	8.4%
4	49	9.8%
5	70	14.0%
6	102	20.4%
7	203	40.7%

Value	Count	Frequency (%)
7	203	40.7%
6	102	20.4%
5	70	14.0%
4	49	9.8%
3	42	8.4%
2	17	3.4%
1	16	3.2%

rank_resp
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	7
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.527054108

Minimum	1
Maximum	7
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	3
Q3	5
95-th percentile	7
Maximum	7
Range	6
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.057596743
Coefficient of variation (CV)	0.5833754402
Kurtosis	-1.187605078
Mean	3.527054108
Median Absolute Deviation (MAD)	2
Skewness	0.3541560179
Sum	1760
Variance	4.233704357
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
1	106	21.2%
2	93	18.6%
3	71	14.2%
4	70	14.0%
7	62	12.4%
6	56	11.2%
5	41	8.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	106	21.2%
2	93	18.6%
3	71	14.2%
4	70	14.0%
5	41	8.2%
6	56	11.2%
7	62	12.4%

Value	Count	Frequency (%)
7	62	12.4%
6	56	11.2%
5	41	8.2%
4	70	14.0%
3	71	14.2%
2	93	18.6%
1	106	21.2%

rank_just
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	7
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4.651302605

Minimum	1
Maximum	7
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	4
median	5
Q3	6
95-th percentile	7
Maximum	7
Range	6
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.671474984
Coefficient of variation (CV)	0.3593563192
Kurtosis	-0.5866613384
Mean	4.651302605
Median Absolute Deviation (MAD)	1
Skewness	-0.4219703321
Sum	2321
Variance	2.793828621
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
5	115	23.0%
6	96	19.2%
4	91	18.2%
7	75	15.0%
3	63	12.6%
2	33	6.6%
1	26	5.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	26	5.2%
2	33	6.6%
3	63	12.6%
4	91	18.2%
5	115	23.0%
6	96	19.2%
7	75	15.0%

Value	Count	Frequency (%)
7	75	15.0%
6	96	19.2%
5	115	23.0%
4	91	18.2%
3	63	12.6%
2	33	6.6%
1	26	5.2%

rank_anony
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	7
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.054108216

Minimum	1
Maximum	7
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	3
Q3	4
95-th percentile	6
Maximum	7
Range	6
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.587816994
Coefficient of variation (CV)	0.5198954594
Kurtosis	-0.4222403
Mean	3.054108216
Median Absolute Deviation (MAD)	1
Skewness	0.6240587048
Sum	1524
Variance	2.521162808
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
2	141	28.3%
3	105	21.0%
1	81	16.2%
4	74	14.8%
5	51	10.2%
6	34	6.8%
7	13	2.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	81	16.2%
2	141	28.3%
3	105	21.0%
4	74	14.8%
5	51	10.2%
6	34	6.8%
7	13	2.6%

Value	Count	Frequency (%)
7	13	2.6%
6	34	6.8%
5	51	10.2%
4	74	14.8%
3	105	21.0%
2	141	28.3%
1	81	16.2%

rank_harms
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	7
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.69739479

Minimum	1
Maximum	7
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1
median	2
Q3	4
95-th percentile	6
Maximum	7
Range	6
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	1.808784129
Coefficient of variation (CV)	0.6705670732
Kurtosis	-0.4651366385
Mean	2.69739479
Median Absolute Deviation (MAD)	1
Skewness	0.8169441724
Sum	1346
Variance	3.271700027
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
1	189	37.9%
2	91	18.2%
3	66	13.2%
4	58	11.6%
5	45	9.0%
6	30	6.0%
7	20	4.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	189	37.9%
2	91	18.2%
3	66	13.2%
4	58	11.6%
5	45	9.0%
6	30	6.0%
7	20	4.0%

Value	Count	Frequency (%)
7	20	4.0%
6	30	6.0%
5	45	9.0%
4	58	11.6%
3	66	13.2%
2	91	18.2%
1	189	37.9%

rank_balance
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	7
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4.975951904

Minimum	1
Maximum	7
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	2
Q1	4
median	5
Q3	6
95-th percentile	7
Maximum	7
Range	6
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.616749319
Coefficient of variation (CV)	0.3249125695
Kurtosis	-0.4443151664
Mean	4.975951904
Median Absolute Deviation (MAD)	1
Skewness	-0.5992454593
Sum	2483
Variance	2.613878359
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
6	122	24.4%
5	111	22.2%
7	97	19.4%
4	68	13.6%
3	58	11.6%
2	28	5.6%
1	15	3.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	15	3.0%
2	28	5.6%
3	58	11.6%
4	68	13.6%
5	111	22.2%
6	122	24.4%
7	97	19.4%

Value	Count	Frequency (%)
7	97	19.4%
6	122	24.4%
5	111	22.2%
4	68	13.6%
3	58	11.6%
2	28	5.6%
1	15	3.0%

rank_pub_interst
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	7
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.573146293

Minimum	1
Maximum	7
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	3
Q3	5
95-th percentile	7
Maximum	7
Range	6
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	1.760841438
Coefficient of variation (CV)	0.4927985854
Kurtosis	-0.9434350009
Mean	3.573146293
Median Absolute Deviation (MAD)	1
Skewness	0.2559463962
Sum	1783
Variance	3.100562571
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
2	96	19.2%
3	94	18.8%
4	89	17.8%
1	66	13.2%
5	66	13.2%
6	59	11.8%
7	29	5.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	66	13.2%
2	96	19.2%
3	94	18.8%
4	89	17.8%
5	66	13.2%
6	59	11.8%
7	29	5.8%

Value	Count	Frequency (%)
7	29	5.8%
6	59	11.8%
5	66	13.2%
4	89	17.8%
3	94	18.8%
2	96	19.2%
1	66	13.2%

rank_add_fac_1
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct	67
Distinct (%)	82.7%
Missing	418
Missing (%)	83.8%
Memory size	4.0 KiB

None	15
carrying out the study in a way that it is not going to be skewed based upon what the user believes the researcher is looking for	1
honesty when disseminating the information	1
inform participants of study after	1
understanding the limits of the internet	1
Other values (62)	62

Length

Max length	336
Median length	163
Mean length	79.72839506
Min length	4

Characters and Unicode

Total characters	6458
Distinct characters	39
Distinct categories	9 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	66 ?
Unique (%)	81.5%

Sample

1st row	a full disclosure of any political organizations of which a researcher belongs to or has donated to within a previous time frame (such as 4 yrs)
2nd row	the researchers should not intrude into the user's personal lives
3rd row	full disclosure of intent of research
4th row	None
5th row	don't intentionally mislead unknowing participants sending them links to a fake fact checking site would be an example 4

Common Values

Value	Count	Frequency (%)
None	15	3.0%
carrying out the study in a way that it is not going to be skewed based upon what the user believes the researcher is looking for	1	0.2%
honesty when disseminating the information	1	0.2%
inform participants of study after	1	0.2%
understanding the limits of the internet	1	0.2%
the social media service (twittet, facebook, etc) knows the data is being collected	1	0.2%
explain where they get the info on people to study did they pay for it?	1	0.2%
methods used to interact with participants whether awareunaware and the expected knowledge of social platforms for modern usersie - using automated bot accounts will likely skew experiment results due to intelligent users identifying bots versus real humans	1	0.2%
usefulness - is the study or experiment actually useful in the sense that its results or process is beneficial to those in the study or to people outside of it, aside from the researchers?	1	0.2%
i think the above list covers everything if there are more things to consider, they're not coming to mind for me, and i've sat here thinking for awhile (timer is down to 12 minutes left, and i have no idea how many more pages are left in this study, so i should probably stop typing here and carry on so i don't run out of time overall)	1	0.2%
Other values (57)	57	11.4%
(Missing)	418	83.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	60	5.6%
to	33	3.1%
of	31	2.9%
be	25	2.3%
and	21	2.0%
for	19	1.8%
is	18	1.7%
that	17	1.6%
study	17	1.6%
or	17	1.6%
Other values (441)	809	75.8%

Most occurring characters

Value	Count	Frequency (%)
	990	15.3%
e	625	9.7%
t	555	8.6%
a	443	6.9%
i	437	6.8%
o	421	6.5%
n	381	5.9%
s	353	5.5%
r	345	5.3%
h	231	3.6%
Other values (29)	1677	26.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	5345	82.8%
Space Separator	990	15.3%
Other Punctuation	66	1.0%
Uppercase Letter	15	0.2%
Close Punctuation	11	0.2%
Open Punctuation	11	0.2%
Dash Punctuation	10	0.2%
Decimal Number	9	0.1%
Final Punctuation	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	625	11.7%
t	555	10.4%
a	443	8.3%
i	437	8.2%
o	421	7.9%
n	381	7.1%
s	353	6.6%
r	345	6.5%
h	231	4.3%
d	204	3.8%
Other values (16)	1350	25.3%

Other Punctuation

Value	Count	Frequency (%)
,	36	54.5%
'	18	27.3%
"	8	12.1%
?	4	6.1%

Decimal Number

Value	Count	Frequency (%)
2	4	44.4%
4	3	33.3%
1	2	22.2%

Space Separator

Value	Count	Frequency (%)
	990	100.0%

Uppercase Letter

Value	Count	Frequency (%)
N	15	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	11	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	11	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	10	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	5360	83.0%
Common	1098	17.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	625	11.7%
t	555	10.4%
a	443	8.3%
i	437	8.2%
o	421	7.9%
n	381	7.1%
s	353	6.6%
r	345	6.4%
h	231	4.3%
d	204	3.8%
Other values (17)	1365	25.5%

Common

Value	Count	Frequency (%)
	990	90.2%
,	36	3.3%
'	18	1.6%
)	11	1.0%
(	11	1.0%
-	10	0.9%
"	8	0.7%
?	4	0.4%
2	4	0.4%
4	3	0.3%
Other values (2)	3	0.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	6457	> 99.9%
Punctuation	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	990	15.3%
e	625	9.7%
t	555	8.6%
a	443	6.9%
i	437	6.8%
o	421	6.5%
n	381	5.9%
s	353	5.5%
r	345	5.3%
h	231	3.6%
Other values (28)	1676	26.0%

Punctuation

Value	Count	Frequency (%)
’	1	100.0%

rank_add_fac_1_pos
Real number (ℝ_≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct	11
Distinct (%)	7.4%
Missing	350
Missing (%)	70.1%
Infinite	0
Infinite (%)	0.0%
Mean	4.429530201

Minimum	0
Maximum	10
Zeros	12
Zeros (%)	2.4%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	4
Q3	8
95-th percentile	8.6
Maximum	10
Range	10
Interquartile range (IQR)	7

Descriptive statistics

Standard deviation	3.323249455
Coefficient of variation (CV)	0.7502487406
Kurtosis	-1.605372616
Mean	4.429530201
Median Absolute Deviation (MAD)	3
Skewness	0.1127040338
Sum	660
Variance	11.04398694
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=11)

Value	Count	Frequency (%)
8	44	8.8%
1	42	8.4%
0	12	2.4%
3	12	2.4%
4	10	2.0%
10	7	1.4%
6	6	1.2%
7	6	1.2%
5	5	1.0%
2	4	0.8%
(Missing)	350	70.1%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	12	2.4%
1	42	8.4%
2	4	0.8%
3	12	2.4%
4	10	2.0%
5	5	1.0%
6	6	1.2%
7	6	1.2%
8	44	8.8%
9	1	0.2%

Value	Count	Frequency (%)
10	7	1.4%
9	1	0.2%
8	44	8.8%
7	6	1.2%
6	6	1.2%
5	5	1.0%
4	10	2.0%
3	12	2.4%
2	4	0.8%
1	42	8.4%

rank_add_fac_2
Categorical

HIGH CORRELATION
MISSING

Distinct	19
Distinct (%)	73.1%
Missing	473
Missing (%)	94.8%
Memory size	4.0 KiB

None	8
interact as a researcher it will carry more weight if people know who is suggesting or informing and this does matter	1
minimize threats to social media users mental health by the study manipulations	1
must be for positive societal movement, not backwards movement	1
researchers must maintain participant confidentiality	1
Other values (14)	14

Length

Max length	132
Median length	79
Mean length	40.30769231
Min length	4

Characters and Unicode

Total characters	1048
Distinct characters	28
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	18 ?
Unique (%)	69.2%

Sample

1st row	None
2nd row	abiding by the laws of research
3rd row	None
4th row	ensure payments alongside bonus
5th row	privacy that their identity will not be known

Common Values

Value	Count	Frequency (%)
None	8	1.6%
interact as a researcher it will carry more weight if people know who is suggesting or informing and this does matter	1	0.2%
minimize threats to social media users mental health by the study manipulations	1	0.2%
must be for positive societal movement, not backwards movement	1	0.2%
researchers must maintain participant confidentiality	1	0.2%
at the conclusion, the user should have the option to have their data dismissed	1	0.2%
laws keep lawsuits to a minimal	1	0.2%
information about who the researchers are	1	0.2%
they should be required to state which political party they make financial contributions to , and how much money they give each year	1	0.2%
be mindful of manipulation that could skewer the results you desire	1	0.2%
Other values (9)	9	1.8%
(Missing)	473	94.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	11	6.3%
none	8	4.6%
to	6	3.4%
be	4	2.3%
and	3	1.7%
of	3	1.7%
they	3	1.7%
study	3	1.7%
are	3	1.7%
movement	2	1.1%
Other values (112)	128	73.6%

Most occurring characters

Value	Count	Frequency (%)
	148	14.1%
e	110	10.5%
t	84	8.0%
a	75	7.2%
i	74	7.1%
s	63	6.0%
o	63	6.0%
n	60	5.7%
r	55	5.2%
h	44	4.2%
Other values (18)	272	26.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	889	84.8%
Space Separator	148	14.1%
Uppercase Letter	8	0.8%
Other Punctuation	3	0.3%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	110	12.4%
t	84	9.4%
a	75	8.4%
i	74	8.3%
s	63	7.1%
o	63	7.1%
n	60	6.7%
r	55	6.2%
h	44	4.9%
l	30	3.4%
Other values (15)	231	26.0%

Space Separator

Value	Count	Frequency (%)
	148	100.0%

Uppercase Letter

Value	Count	Frequency (%)
N	8	100.0%

Other Punctuation

Value	Count	Frequency (%)
,	3	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	897	85.6%
Common	151	14.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	110	12.3%
t	84	9.4%
a	75	8.4%
i	74	8.2%
s	63	7.0%
o	63	7.0%
n	60	6.7%
r	55	6.1%
h	44	4.9%
l	30	3.3%
Other values (16)	239	26.6%

Common

Value	Count	Frequency (%)
	148	98.0%
,	3	2.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1048	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	148	14.1%
e	110	10.5%
t	84	8.0%
a	75	7.2%
i	74	7.1%
s	63	6.0%
o	63	6.0%
n	60	5.7%
r	55	5.2%
h	44	4.2%
Other values (18)	272	26.0%

rank_add_fac_2_pos
Real number (ℝ_≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct	10
Distinct (%)	11.5%
Missing	412
Missing (%)	82.6%
Infinite	0
Infinite (%)	0.0%
Mean	5.172413793

Minimum	0
Maximum	10
Zeros	11
Zeros (%)	2.2%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	2
median	5
Q3	9
95-th percentile	10
Maximum	10
Range	10
Interquartile range (IQR)	7

Descriptive statistics

Standard deviation	3.84044237
Coefficient of variation (CV)	0.7424855248
Kurtosis	-1.808645945
Mean	5.172413793
Median Absolute Deviation (MAD)	4
Skewness	-0.05284867363
Sum	450
Variance	14.74899759
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=10)

Value	Count	Frequency (%)
9	29	5.8%
2	20	4.0%
0	11	2.2%
1	8	1.6%
10	7	1.4%
5	4	0.8%
8	4	0.8%
3	2	0.4%
7	1	0.2%
6	1	0.2%
(Missing)	412	82.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	11	2.2%
1	8	1.6%
2	20	4.0%
3	2	0.4%
5	4	0.8%
6	1	0.2%
7	1	0.2%
8	4	0.8%
9	29	5.8%
10	7	1.4%

Value	Count	Frequency (%)
10	7	1.4%
9	29	5.8%
8	4	0.8%
7	1	0.2%
6	1	0.2%
5	4	0.8%
3	2	0.4%
2	20	4.0%
1	8	1.6%
0	11	2.2%

rank_add_fac_3
Categorical

HIGH CORRELATION
MISSING

Distinct	19
Distinct (%)	86.4%
Missing	477
Missing (%)	95.6%
Memory size	4.0 KiB

None	4
ethical application in the real world	1
cost vs benefits for study participants will they be compensated in some way?	1
inform participants about the study's outcomes after the study has been concluded and analyzed	1
length of study and data collection	1
Other values (14)	14

Length

Max length	94
Median length	63.5
Mean length	43.36363636
Min length	4

Characters and Unicode

Total characters	954
Distinct characters	33
Distinct categories	7 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	18 ?
Unique (%)	81.8%

Sample

1st row	give results after entire experiment is done
2nd row	confidentiality of participants information
3rd row	None
4th row	collection of data that is personal
5th row	promise to offer access to study results ifwhen available

Common Values

Value	Count	Frequency (%)
None	4	0.8%
ethical application in the real world	1	0.2%
cost vs benefits for study participants will they be compensated in some way?	1	0.2%
inform participants about the study's outcomes after the study has been concluded and analyzed	1	0.2%
length of study and data collection	1	0.2%
all unknowing participants notified of use of their info	1	0.2%
research misconduct, such as:fabrication, falsification, and plagiarism	1	0.2%
size of the study how many people are they collecting data on	1	0.2%
the user should be given the results of the study when it is over	1	0.2%
adherence to regulations (like gdpr)	1	0.2%
Other values (9)	9	1.8%
(Missing)	477	95.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	11	7.1%
of	8	5.2%
study	6	3.9%
none	4	2.6%
is	4	2.6%
participants	4	2.6%
and	4	2.6%
to	4	2.6%
data	3	1.9%
are	3	1.9%
Other values (91)	103	66.9%

Most occurring characters

Value	Count	Frequency (%)
	133	13.9%
e	93	9.7%
t	87	9.1%
i	73	7.7%
n	63	6.6%
a	62	6.5%
o	61	6.4%
s	54	5.7%
r	42	4.4%
l	34	3.6%
Other values (23)	252	26.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	808	84.7%
Space Separator	133	13.9%
Other Punctuation	6	0.6%
Uppercase Letter	4	0.4%
Open Punctuation	1	0.1%
Close Punctuation	1	0.1%
Final Punctuation	1	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	93	11.5%
t	87	10.8%
i	73	9.0%
n	63	7.8%
a	62	7.7%
o	61	7.5%
s	54	6.7%
r	42	5.2%
l	34	4.2%
h	33	4.1%
Other values (14)	206	25.5%

Other Punctuation

Value	Count	Frequency (%)
,	3	50.0%
'	1	16.7%
?	1	16.7%
:	1	16.7%

Space Separator

Value	Count	Frequency (%)
	133	100.0%

Uppercase Letter

Value	Count	Frequency (%)
N	4	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	1	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	1	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	812	85.1%
Common	142	14.9%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	93	11.5%
t	87	10.7%
i	73	9.0%
n	63	7.8%
a	62	7.6%
o	61	7.5%
s	54	6.7%
r	42	5.2%
l	34	4.2%
h	33	4.1%
Other values (15)	210	25.9%

Common

Value	Count	Frequency (%)
	133	93.7%
,	3	2.1%
'	1	0.7%
?	1	0.7%
:	1	0.7%
(	1	0.7%
)	1	0.7%
’	1	0.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	953	99.9%
Punctuation	1	0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	133	14.0%
e	93	9.8%
t	87	9.1%
i	73	7.7%
n	63	6.6%
a	62	6.5%
o	61	6.4%
s	54	5.7%
r	42	4.4%
l	34	3.6%
Other values (22)	251	26.3%

Punctuation

Value	Count	Frequency (%)
’	1	100.0%

rank_add_fac_3_pos
Real number (ℝ_≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct	9
Distinct (%)	11.0%
Missing	417
Missing (%)	83.6%
Infinite	0
Infinite (%)	0.0%
Mean	5.817073171

Minimum	0
Maximum	10
Zeros	11
Zeros (%)	2.2%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	2
median	7
Q3	10
95-th percentile	10
Maximum	10
Range	10
Interquartile range (IQR)	8

Descriptive statistics

Standard deviation	4.079845219
Coefficient of variation (CV)	0.7013570397
Kurtosis	-1.742930501
Mean	5.817073171
Median Absolute Deviation (MAD)	3
Skewness	-0.181235257
Sum	477
Variance	16.64513701
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=9)

Value	Count	Frequency (%)
10	32	6.4%
3	15	3.0%
0	11	2.2%
1	7	1.4%
9	5	1.0%
8	4	0.8%
2	4	0.8%
6	2	0.4%
4	2	0.4%
(Missing)	417	83.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	11	2.2%
1	7	1.4%
2	4	0.8%
3	15	3.0%
4	2	0.4%
6	2	0.4%
8	4	0.8%
9	5	1.0%
10	32	6.4%

Value	Count	Frequency (%)
10	32	6.4%
9	5	1.0%
8	4	0.8%
6	2	0.4%
4	2	0.4%
3	15	3.0%
2	4	0.8%
1	7	1.4%
0	11	2.2%

aware_sm_advan_score
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct	8
Distinct (%)	1.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.869739479

Minimum	-3
Maximum	4
Zeros	47
Zeros (%)	9.4%
Negative	21
Negative (%)	4.2%
Memory size	4.0 KiB

Quantile statistics

Minimum	-3
5-th percentile	0
Q1	1
median	2
Q3	3
95-th percentile	3
Maximum	4
Range	7
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.212827247
Coefficient of variation (CV)	0.6486610894
Kurtosis	0.112523089
Mean	1.869739479
Median Absolute Deviation (MAD)	1
Skewness	-0.6708705122
Sum	933
Variance	1.470949932
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=8)

Value	Count	Frequency (%)
3	167	33.5%
2	141	28.3%
1	106	21.2%
0	47	9.4%
-1	19	3.8%
4	17	3.4%
-2	1	0.2%
-3	1	0.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
-3	1	0.2%
-2	1	0.2%
-1	19	3.8%
0	47	9.4%
1	106	21.2%
2	141	28.3%
3	167	33.5%
4	17	3.4%

Value	Count	Frequency (%)
4	17	3.4%
3	167	33.5%
2	141	28.3%
1	106	21.2%
0	47	9.4%
-1	19	3.8%
-2	1	0.2%
-3	1	0.2%

aware_sm_interact_score
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

1	156
2	146
0	117
3	68
-1	12

Length

Max length	2
Median length	1
Mean length	1.024048096
Min length	1

Characters and Unicode

Total characters	511
Distinct characters	5
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	2
4th row	1
5th row	3

Common Values

Value	Count	Frequency (%)
1	156	31.3%
2	146	29.3%
0	117	23.4%
3	68	13.6%
-1	12	2.4%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
1	168	33.7%
2	146	29.3%
0	117	23.4%
3	68	13.6%

Most occurring characters

Value	Count	Frequency (%)
1	168	32.9%
2	146	28.6%
0	117	22.9%
3	68	13.3%
-	12	2.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	499	97.7%
Dash Punctuation	12	2.3%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	168	33.7%
2	146	29.3%
0	117	23.4%
3	68	13.6%

Dash Punctuation

Value	Count	Frequency (%)
-	12	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	511	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	168	32.9%
2	146	28.6%
0	117	22.9%
3	68	13.3%
-	12	2.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	511	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	168	32.9%
2	146	28.6%
0	117	22.9%
3	68	13.3%
-	12	2.3%

aware_sm_use_score
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	10
Distinct (%)	2.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	6.569138277

Minimum	0
Maximum	9
Zeros	5
Zeros (%)	1.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	0
5-th percentile	2
Q1	5
median	7
Q3	9
95-th percentile	9
Maximum	9
Range	9
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	2.368791708
Coefficient of variation (CV)	0.3605939787
Kurtosis	-0.4509960942
Mean	6.569138277
Median Absolute Deviation (MAD)	2
Skewness	-0.6818309309
Sum	3278
Variance	5.611174156
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=10)

Value	Count	Frequency (%)
9	167	33.5%
5	61	12.2%
7	59	11.8%
6	55	11.0%
8	52	10.4%
4	47	9.4%
3	31	6.2%
1	14	2.8%
2	8	1.6%
0	5	1.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	5	1.0%
1	14	2.8%
2	8	1.6%
3	31	6.2%
4	47	9.4%
5	61	12.2%
6	55	11.0%
7	59	11.8%
8	52	10.4%
9	167	33.5%

Value	Count	Frequency (%)
9	167	33.5%
8	52	10.4%
7	59	11.8%
6	55	11.0%
5	61	12.2%
4	47	9.4%
3	31	6.2%
2	8	1.6%
1	14	2.8%
0	5	1.0%

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

First rows

	df_index	sm_use	age	gender_id	ethnic_id	edu	politic_views	aware_sm_res	aware_sm_advan	aware_sm_interact	aware_sm_use	ethic_appr	study_1_ethic_acc	study_1_conc	study_1_add_info	study_2_ethic_acc	study_2_conc	study_2_add_info	study_3_ethic_acc	study_3_conc	study_3_add_info	study_4_ethic_acc	study_4_conc	study_4_add_info	design_cont	design_num_users	design_res_purp	design_len_data	design_admin_inter	design_inter_type	design_partic_aware	design_inter_impact	design_type_data	design_add_fac	rank_sci_repro	rank_resp	rank_just	rank_anony	rank_harms	rank_balance	rank_pub_interst	rank_add_fac_1	rank_add_fac_1_pos	rank_add_fac_2	rank_add_fac_2_pos	rank_add_fac_3	rank_add_fac_3_pos	aware_sm_advan_score	aware_sm_interact_score	aware_sm_use_score
0	1	Facebook	29.0	Male	Asian - Eastern	Highschool	Slightly liberal	Extremely aware	[… are large and can contain millions of data points, … reflect events in real-time and can be collected continuously over time, … are naturalistic in that they do not require researchers to directly interact with research volunteers, … often capture social relationships not found using traditional methods (e.g. surveys)]	[Creating fake accounts ("bots"), Secretly changing the content of what users see]	[Political elections (e.g. voting behavior), Economic forecasting, Presidential approval ratings, Health topics (e.g. spread of diseases), Well-being and economic satisfaction, Communication (e.g. spread of opinions and hate-speech), Public sentiment (e.g. environment-related concerns), News consumption (e.g. sharing of misinformation), Social networks]	The scope of the project and actions there in do not cross certain boundaries that may purposefully negatively affect participants as well as legal regulations and standard practices.	Neutral	NaN	NaN	Neutral	NaN	NaN	Neutral	NaN	NaN	Neutral	NaN	NaN	Not at all important	Not at all important	Not at all important	Not at all important	Not at all important	Slightly important	Slightly important	Not at all important	Not at all important	NaN	2.0	7.0	5.0	6.0	4.0	3.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN	4	0	9
1	2	Twitter	33.0	Male	Mixed race	Highschool	Neutral/ Neither conservative or liberal	Moderately aware	[… are large and can contain millions of data points]	[Privately messaging users, Publicly posting on users' profiles, Secretly changing the content of what users see]	[Political elections (e.g. voting behavior), Economic forecasting, Presidential approval ratings, Health topics (e.g. spread of diseases), Well-being and economic satisfaction, Communication (e.g. spread of opinions and hate-speech), Public sentiment (e.g. environment-related concerns), News consumption (e.g. sharing of misinformation), Social networks]	I think Ethical Approval means that the experiment is gathering data without harm or injury to people.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Neutral	NaN	NaN	Not at all important	Not at all important	Not at all important	Not at all important	Not at all important	Not at all important	Moderately important	Not at all important	Not at all important	the only aspects of social media research that would cause concern for me is saving photographs or imaging data	3.0	5.0	2.0	6.0	1.0	7.0	4.0	NaN	NaN	NaN	NaN	NaN	NaN	1	1	9
2	3	Facebook	33.0	Female	Pacific Islander	Bachelor's degree	Very liberal	Extremely aware	[… are large and can contain millions of data points, … are naturalistic in that they do not require researchers to directly interact with research volunteers, … often capture social relationships not found using traditional methods (e.g. surveys), … are readily accessible to researchers and easy to collect]	[Privately messaging users, Publicly posting on users' profiles, Creating fake accounts ("bots"), Secretly changing the content of what users see]	[Political elections (e.g. voting behavior), Presidential approval ratings, Communication (e.g. spread of opinions and hate-speech), News consumption (e.g. sharing of misinformation), Social networks]	Researchers focus on ethical standards towards those they gain data from. They need approval of their approach and receive methods.	NaN	no concerns i would have loved to partake in this study in terms of watching the results	NaN	NaN	going to the poster privately provided opportunity for change without the possibly of increased toxicity from users i prefer this method over commenting the "correct information"	NaN	Somewhat acceptable	i find this is ethical as long as participants were fully aware of what was being monitored the results are interesting! no concerns	NaN	Somewhat unacceptable	i am uncertain how i feel completely about a researcher creating a fake account however i do understand the desire to protect themselves and to not give away their actions as being part of a study this misinformation needed to be corrected for the public but it opened the original poster to toxicity the op may not have known it was incorrect	the researchers had a purpose in seeing the responses of those interacting with the post i do not agree with how it was done entirely however i do not know a better way to get the results that were desired	Extremely important	Very important	Very important	Extremely important	Moderately important	Very important	Moderately important	Extremely important	Not at all important	none that i can think of, other than what has been asked already	7.0	5.0	6.0	3.0	2.0	4.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN	2	2	5
3	4	Facebook	73.0	Female	White / Caucasian	Highschool	Slightly conservative	Moderately aware	[… are large and can contain millions of data points, … often capture social relationships not found using traditional methods (e.g. surveys), … are readily accessible to researchers and easy to collect]	[Creating fake accounts ("bots")]	[Political elections (e.g. voting behavior), Presidential approval ratings, Health topics (e.g. spread of diseases), Communication (e.g. spread of opinions and hate-speech), Public sentiment (e.g. environment-related concerns), News consumption (e.g. sharing of misinformation)]	I would think that using "ethical approval" means that the things others collect on social media sites would need to be honest and moral. Hopefully, there would be no under-handedness used in collecting information.	Neutral	i feel if people know they are being judged they will act, speak, or write differently than if they don't know they are being analyzed	NaN	Somewhat acceptable	i feel as though, in the above case, users had a choice to respond or not so i think it was honest	NaN	Somewhat acceptable	as long as the facebook users were informed that they would be in a study i feel it is fair it was up to the users whether they wanted to participate or not also, they were encouraged, but not actually made to like the facebook study	NaN	Somewhat unacceptable	users were not aware of what was going on so they were possibly more honest in their opinions because they had no idea they were being analyzed	NaN	Moderately important	Moderately important	Extremely important	Very important	Moderately important	Very important	Extremely important	Very important	Very important	reducing any type of hate is always a good thing	7.0	2.0	6.0	3.0	4.0	5.0	1.0	NaN	8.0	NaN	NaN	NaN	NaN	1	1	6
4	5	Twitter	27.0	Female	Native-American	Highschool	Very liberal	Extremely aware	[… often capture social relationships not found using traditional methods (e.g. surveys), … are readily accessible to researchers and easy to collect]	[Privately messaging users, Publicly posting on users' profiles, Creating fake accounts ("bots")]	[Political elections (e.g. voting behavior), Economic forecasting, Presidential approval ratings, Health topics (e.g. spread of diseases), Well-being and economic satisfaction, Communication (e.g. spread of opinions and hate-speech), Public sentiment (e.g. environment-related concerns), News consumption (e.g. sharing of misinformation), Social networks]	A set of rules of what to do and what to not do.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	the web extension being used was invasive, even if it was used with consent the people participating in the study are not educated enough on exactly how much information the web extension was taking	making the source code for the web extension publicly available to have complete transparency over what the extension was doing	NaN	NaN	NaN	Extremely important	Not at all important	Not at all important	Not at all important	Not at all important	Extremely important	Slightly important	Not at all important	Extremely important	NaN	3.0	1.0	5.0	2.0	4.0	6.0	7.0	NaN	NaN	NaN	NaN	NaN	NaN	0	3	9
5	6	Facebook	49.0	Female	Hispanic	Bachelor's degree	Slightly liberal	Slightly aware	[… are large and can contain millions of data points, … reflect events in real-time and can be collected continuously over time, … often capture social relationships not found using traditional methods (e.g. surveys), … are readily accessible to researchers and easy to collect]	[Publicly posting on users' profiles, Creating fake accounts ("bots"), Hacking into users' accounts]	[Political elections (e.g. voting behavior), Health topics (e.g. spread of diseases), Well-being and economic satisfaction, News consumption (e.g. sharing of misinformation), Social networks]	is when the participants have the right to know who was access to their data and what is being done with it.	Somewhat acceptable	NaN	NaN	Somewhat unacceptable	NaN	NaN	NaN	NaN	NaN	Somewhat acceptable	NaN	NaN	Very important	Moderately important	Extremely important	Very important	Very important	Very important	Very important	Very important	Very important	NaN	7.0	2.0	6.0	4.0	1.0	3.0	5.0	NaN	NaN	NaN	NaN	NaN	NaN	2	1	5
6	7	Facebook	53.0	Male	White / Caucasian	Highschool	Slightly conservative	Slightly aware	[… are large and can contain millions of data points, … reflect events in real-time and can be collected continuously over time, … are naturalistic in that they do not require researchers to directly interact with research volunteers, … often capture social relationships not found using traditional methods (e.g. surveys), … are readily accessible to researchers and easy to collect, … are unaffected by the way social media platforms work]	[None of the above]	[Political elections (e.g. voting behavior), Presidential approval ratings, Communication (e.g. spread of opinions and hate-speech), Public sentiment (e.g. environment-related concerns), News consumption (e.g. sharing of misinformation)]	Verification of some sort that social media users and/or the data being used is not being skewed to support a theory or the results in any way.	Completey unacceptable	easy enough for an outside government to try copying such a study with the sole purpose of creating much more polarization, hate, etc not that it hasn't been tried and tested perhaps innumerable times by all types of foreign or domestic entities as far as we know no actual study would have really been needed to know that using a type of marketing manipulation could alter the recipients moodlevels of concernanxietyhateetc	NaN	Somewhat acceptable	NaN	concerns over the possibility of the researchers having their own political agenda yet fake news is a major problem what social media really is when mass sharing news (political news), is simple propaganda from the left and right	Neutral	the researchers seem in some ways to try manipulating political viewpoints in a segment of the population for the sake of science	NaN	Neutral	many of the people that have large political followings on twitter (and many who don't) often know already the news they are sharing is fake it's political partisanship and the spreading of propaganda some might post fake news only to gain more followers (the masses) if they believe it serves that end	NaN	Moderately important	Extremely important	Slightly important	Very important	Moderately important	Extremely important	Extremely important	Extremely important	Very important	na i have already voiced my concerns about researching this in general from this surveys other questions	6.0	7.0	5.0	3.0	2.0	4.0	1.0	a full disclosure of any political organizations of which a researcher belongs to or has donated to within a previous time frame (such as 4 yrs)	8.0	NaN	9.0	NaN	10.0	2	0	5
7	8	Reddit	29.0	Female	White / Caucasian	Highschool	Slightly liberal	Moderately aware	[… are large and can contain millions of data points, … reflect events in real-time and can be collected continuously over time, … are naturalistic in that they do not require researchers to directly interact with research volunteers, … often capture social relationships not found using traditional methods (e.g. surveys), … are readily accessible to researchers and easy to collect]	[Privately messaging users, Publicly posting on users' profiles, Creating fake accounts ("bots")]	[Political elections (e.g. voting behavior), Economic forecasting, Well-being and economic satisfaction, Communication (e.g. spread of opinions and hate-speech), Public sentiment (e.g. environment-related concerns), News consumption (e.g. sharing of misinformation)]	Going through a process of peer review maybe? Like earlier you mentioned creating bot accounts, so maybe making sure the researcher isn’t spreading hate or misinformation	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Somewhat acceptable	NaN	NaN	Very important	Very important	Moderately important	Extremely important	Moderately important	Moderately important	Moderately important	Extremely important	Very important	the possibility of bot accounts spreading misinformation or hate speech just for the purpose of an experiment	7.0	6.0	5.0	4.0	2.0	1.0	3.0	NaN	NaN	NaN	NaN	NaN	NaN	3	3	6
8	9	Facebook	23.0	Male	White / Caucasian	Bachelor's degree	Neutral/ Neither conservative or liberal	Moderately aware	[… are unaffected by the way social media platforms work]	[Publicly posting on users' profiles]	[Social networks]	Social media is a collective term for websites and applications that focus on communication, community-based input, interaction, content-sharing and collaboration.	Neutral	NaN	NaN	Neutral	NaN	NaN	Neutral	NaN	NaN	Neutral	NaN	NaN	Moderately important	Moderately important	Moderately important	Moderately important	Moderately important	Moderately important	Moderately important	Moderately important	Moderately important	no, i didn't anything like that	5.0	4.0	7.0	2.0	6.0	3.0	1.0	NaN	6.0	NaN	5.0	NaN	8.0	-1	1	1
9	10	Facebook	65.0	Male	Hispanic	Highschool	Very liberal	Moderately aware	[… are large and can contain millions of data points, … reflect events in real-time and can be collected continuously over time, … are naturalistic in that they do not require researchers to directly interact with research volunteers, … often capture social relationships not found using traditional methods (e.g. surveys), … are readily accessible to researchers and easy to collect]	[Privately messaging users, Creating fake accounts ("bots")]	[Political elections (e.g. voting behavior), Presidential approval ratings, Health topics (e.g. spread of diseases), Well-being and economic satisfaction, Communication (e.g. spread of opinions and hate-speech), Public sentiment (e.g. environment-related concerns), News consumption (e.g. sharing of misinformation), Social networks]	Whether or not something goes against someone's right to privacy online.	NaN	NaN	i would be interested to know what kind of messages they sent the hate speech users that got them to change their minds	NaN	it's perfectly within someone's right to send someone else a message on any platform, therefore i believe this study was acceptable	NaN	NaN	people willingly consented to being part of the research study, so i believe the study was completely acceptable	NaN	NaN	NaN	NaN	Very important	Not at all important	Slightly important	Not at all important	Not at all important	Not at all important	Moderately important	Very important	Not at all important	NaN	5.0	3.0	7.0	4.0	1.0	6.0	2.0	the researchers should not intrude into the user's personal lives	8.0	NaN	NaN	NaN	NaN	3	2	8

Last rows

	df_index	sm_use	age	gender_id	ethnic_id	edu	politic_views	aware_sm_res	aware_sm_advan	aware_sm_interact	aware_sm_use	ethic_appr	study_1_ethic_acc	study_1_conc	study_1_add_info	study_2_ethic_acc	study_2_conc	study_2_add_info	study_3_ethic_acc	study_3_conc	study_3_add_info	study_4_ethic_acc	study_4_conc	study_4_add_info	design_cont	design_num_users	design_res_purp	design_len_data	design_admin_inter	design_inter_type	design_partic_aware	design_inter_impact	design_type_data	design_add_fac	rank_sci_repro	rank_resp	rank_just	rank_anony	rank_harms	rank_balance	rank_pub_interst	rank_add_fac_1	rank_add_fac_1_pos	rank_add_fac_2	rank_add_fac_2_pos	rank_add_fac_3	rank_add_fac_3_pos	aware_sm_advan_score	aware_sm_interact_score	aware_sm_use_score
489	490	Facebook	37.0	Female	White / Caucasian	Master's degree or above	Slightly liberal	Very aware	[… are large and can contain millions of data points, … reflect events in real-time and can be collected continuously over time, … often capture social relationships not found using traditional methods (e.g. surveys), … are readily accessible to researchers and easy to collect]	[None of the above]	[Political elections (e.g. voting behavior), Economic forecasting, Presidential approval ratings, Health topics (e.g. spread of diseases), Well-being and economic satisfaction, Communication (e.g. spread of opinions and hate-speech), Public sentiment (e.g. environment-related concerns), News consumption (e.g. sharing of misinformation), Social networks]	That the researchers would use the acquired users data in an ethical manner without manipulating it. Keeping the users data safe and secure.	Somewhat acceptable	accounts were anonymous and operated by the human which is fine and the outcome was awesome so i would say that type of research is somewhat acceptable	NaN	Somewhat unacceptable	creating fake accounts and sending unsolicited private messages to users is unethical	NaN	NaN	the researchers tried to bribe and manipulate the social media users	i do not approve this practice by the researchers	Somewhat unacceptable	creating fake accounts for research or any other purposes is not acceptable or ethical to me	NaN	Very important	Very important	Extremely important	Extremely important	Extremely important	Very important	Extremely important	Extremely important	Extremely important	None	7.0	3.0	1.0	2.0	6.0	5.0	4.0	NaN	NaN	NaN	NaN	NaN	NaN	2	0	9
490	491	Twitter	44.0	Male	African-American	Highschool	Slightly liberal	Moderately aware	[… are large and can contain millions of data points, … reflect events in real-time and can be collected continuously over time, … are naturalistic in that they do not require researchers to directly interact with research volunteers, … often capture social relationships not found using traditional methods (e.g. surveys), … are readily accessible to researchers and easy to collect]	[Privately messaging users, Publicly posting on users' profiles, Creating fake accounts ("bots")]	[Political elections (e.g. voting behavior), Economic forecasting, Presidential approval ratings, Health topics (e.g. spread of diseases), Well-being and economic satisfaction, Communication (e.g. spread of opinions and hate-speech), Public sentiment (e.g. environment-related concerns), News consumption (e.g. sharing of misinformation), Social networks]	To be approved by the original source it came from.	NaN	this should be done more often, it's a good thing to do, completely acceptable	hate speech is a serious issue, we need to do better	Somewhat acceptable	i think it's in their best concerns to reduce the amount of misinformation, and also help fact check what's posted	it's acceptable on my behalf due to the researchers posting facts	Somewhat acceptable	i can relate to going to another news source to see what information they're giving, and doing this study in this type of way is intriguing	NaN	Neutral	i'm not sure if this is good, or bad	NaN	Extremely important	Extremely important	Extremely important	Extremely important	Very important	Extremely important	Very important	Extremely important	Extremely important	who the researchers are targeting on social media sites race, sex, job type, political view	2.0	4.0	5.0	7.0	3.0	1.0	6.0	NaN	NaN	NaN	NaN	NaN	NaN	3	3	9
491	492	Facebook	39.0	Female	White / Caucasian	Highschool	Slightly conservative	Extremely aware	[… are large and can contain millions of data points, … reflect events in real-time and can be collected continuously over time, … are naturalistic in that they do not require researchers to directly interact with research volunteers, … often capture social relationships not found using traditional methods (e.g. surveys), … are readily accessible to researchers and easy to collect]	[Privately messaging users, Creating fake accounts ("bots"), Secretly changing the content of what users see]	[Political elections (e.g. voting behavior), Presidential approval ratings, Health topics (e.g. spread of diseases), Well-being and economic satisfaction, Communication (e.g. spread of opinions and hate-speech), Public sentiment (e.g. environment-related concerns), News consumption (e.g. sharing of misinformation)]	It means that possible risks have been considered and deemed acceptable.	Completey unacceptable	participants should have the right to accept or decline to participate in the study	NaN	NaN	participants should be made aware of the study and have the option to either accept or decline being included in it	NaN	NaN	NaN	NaN	NaN	the participants should have been made aware that they were part of a study and either accept or decline taking part in it	NaN	Very important	Very important	Very important	Extremely important	Moderately important	Moderately important	Extremely important	Very important	Very important	NaN	7.0	1.0	4.0	2.0	3.0	5.0	6.0	NaN	NaN	NaN	NaN	NaN	NaN	3	1	7
492	493	Reddit	54.0	Male	White / Caucasian	Bachelor's degree	Neutral/ Neither conservative or liberal	Slightly aware	[… are large and can contain millions of data points, … reflect events in real-time and can be collected continuously over time, … often capture social relationships not found using traditional methods (e.g. surveys), … are readily accessible to researchers and easy to collect]	[Privately messaging users, Publicly posting on users' profiles, Creating fake accounts ("bots")]	[Political elections (e.g. voting behavior), Presidential approval ratings, Health topics (e.g. spread of diseases), Well-being and economic satisfaction, Communication (e.g. spread of opinions and hate-speech), Public sentiment (e.g. environment-related concerns), News consumption (e.g. sharing of misinformation), Social networks]	Assurance that the experimenters will use the data and information collected only for the purpose explained in the study. Also, that the person being polled is aware of their rights and redresses, if necessary, by a board or body overseeing the researchers. Generally, that the experiment will cause no foreseeable harm to the people being polled.	Neutral	it's concerning that the study misrepresented the nature of the anonymous accounts who replied to the message it's understandable that they wanted sincere reactions to the messages they sent and that informing the recipients they weren't real people could have caused the messages to be disregarded or met with a level of denial, but since there were a range of responses to the original hate speech, i wonder if any of the replies were incendiary, which could cause the original user to get even more emotionally involved, stressed, or angry, which could lead to actual violence or emotional distress i'd imagine if they were trying to measure how people reacted to different messages they would have to have them grouped into at least empathetic, neutral, and contrary types of messages the researchers sent	it's hard to judge without seeing the actual content of the messages, so i'd want to see that and who is overseeing the study and how closely it's being monitored	Somewhat acceptable	same as the others, that the subjects were unaware of the experiment i do find the anonymous accounts more acceptable than the human-looking automated accounts	NaN	NaN	NaN	it seems the study was forthcoming and transparent and that participants had to opt in to join it, so i can't see any issues, as long as all other processes are in place (eg the study is being overseen, etc)	Somewhat acceptable	while most of the study seems innocuous, for instance, the bot is just replying with a tweet about fact-checking that the user can choose not to click, it's always concerning when the subjects don't know they're part of an experiment and that the automated accounts were apparently made to look like a human user	i'd want to make sure the study is only using publicly-made twitter statements and not going any further by looking into other social media sites the user might have linked or any other biographical information that could be discerned	Slightly important	Not at all important	Slightly important	Not at all important	Very important	Slightly important	Extremely important	Not at all important	Extremely important	NaN	7.0	2.0	5.0	6.0	1.0	3.0	4.0	NaN	NaN	NaN	NaN	NaN	NaN	2	3	8
493	494	Facebook	32.0	Male	White / Caucasian	Master's degree or above	Slightly conservative	Very aware	[… are large and can contain millions of data points, … reflect events in real-time and can be collected continuously over time, … are naturalistic in that they do not require researchers to directly interact with research volunteers, … often capture social relationships not found using traditional methods (e.g. surveys), … are readily accessible to researchers and easy to collect]	[Creating fake accounts ("bots")]	[Political elections (e.g. voting behavior), Economic forecasting, Presidential approval ratings, Health topics (e.g. spread of diseases), Well-being and economic satisfaction, Communication (e.g. spread of opinions and hate-speech), Public sentiment (e.g. environment-related concerns), News consumption (e.g. sharing of misinformation), Social networks]	Ethical approval means getting approval from the University or the government or both.	NaN	NaN	NaN	NaN	NaN	some people may not want to be contacted privately	NaN	choosing between the results of their own data or money is completely unacceptable why should participants have to pay to view their own data? they created it, so they should have access to it if they want it	NaN	NaN	NaN	NaN	Very important	Very important	Very important	Slightly important	Very important	Extremely important	Very important	Extremely important	Extremely important	NaN	7.0	2.0	5.0	1.0	4.0	3.0	6.0	NaN	NaN	NaN	NaN	NaN	NaN	3	1	9
494	495	Facebook	35.0	Female	White / Caucasian	Bachelor's degree	Neutral/ Neither conservative or liberal	Moderately aware	[… are large and can contain millions of data points, … reflect events in real-time and can be collected continuously over time, … often capture social relationships not found using traditional methods (e.g. surveys), … are readily accessible to researchers and easy to collect]	[Creating fake accounts ("bots"), Secretly changing the content of what users see]	[Political elections (e.g. voting behavior), Economic forecasting, Presidential approval ratings, Health topics (e.g. spread of diseases), Well-being and economic satisfaction, Communication (e.g. spread of opinions and hate-speech), Public sentiment (e.g. environment-related concerns), News consumption (e.g. sharing of misinformation), Social networks]	Approval to do any type of thing that might be deceptive.	NaN	NaN	NaN	Somewhat unacceptable	it seems a little too deceptive to me	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Moderately important	Moderately important	Very important	Very important	Very important	Very important	Extremely important	Very important	Very important	NaN	6.0	5.0	4.0	1.0	3.0	7.0	2.0	NaN	NaN	NaN	NaN	NaN	NaN	2	0	9
495	496	Facebook	39.0	Male	White / Caucasian	Master's degree or above	Very conservative	Moderately aware	[… reflect events in real-time and can be collected continuously over time, … are naturalistic in that they do not require researchers to directly interact with research volunteers, … often capture social relationships not found using traditional methods (e.g. surveys), … are readily accessible to researchers and easy to collect, … are always representative of people’s offline behavior, … are unaffected by the way social media platforms work]	[Publicly posting on users' profiles]	[Health topics (e.g. spread of diseases), Well-being and economic satisfaction, Communication (e.g. spread of opinions and hate-speech), Public sentiment (e.g. environment-related concerns), News consumption (e.g. sharing of misinformation), Social networks]	It has to with researchers taking a mental note of the standards meant to be followed while conducting research experiment.	NaN	NaN	NaN	Somewhat acceptable	NaN	NaN	Somewhat acceptable	NaN	NaN	NaN	NaN	NaN	Extremely important	Slightly important	Extremely important	Moderately important	Slightly important	Extremely important	Extremely important	Slightly important	Very important	i can't think of any other aspects	7.0	3.0	2.0	4.0	1.0	5.0	6.0	NaN	NaN	NaN	NaN	NaN	NaN	0	1	6
496	497	Facebook	37.0	Female	African-American	Highschool	Very liberal	Not at all aware	[… often capture social relationships not found using traditional methods (e.g. surveys), … are readily accessible to researchers and easy to collect]	[Creating fake accounts ("bots")]	[Political elections (e.g. voting behavior), Communication (e.g. spread of opinions and hate-speech), News consumption (e.g. sharing of misinformation), Social networks]	I think ethical approval means that institutions have to deem the experiments as tests that most would approve of.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Not at all important	Not at all important	Not at all important	Not at all important	Not at all important	Not at all important	Slightly important	Not at all important	Not at all important	NaN	7.0	4.0	6.0	3.0	1.0	2.0	5.0	NaN	NaN	NaN	NaN	NaN	NaN	0	1	4
497	498	Reddit	23.0	Female	African-American	Highschool	Slightly liberal	Slightly aware	[… are large and can contain millions of data points, … often capture social relationships not found using traditional methods (e.g. surveys), … are readily accessible to researchers and easy to collect]	[Privately messaging users, Publicly posting on users' profiles]	[Political elections (e.g. voting behavior), Economic forecasting, Presidential approval ratings, Well-being and economic satisfaction, Communication (e.g. spread of opinions and hate-speech), Public sentiment (e.g. environment-related concerns), News consumption (e.g. sharing of misinformation), Social networks]	I think ethical approval means that the experiment has to be deemed as appropriate, safe, and not have long-term consequences.	Somewhat unacceptable	it is unacceptable that the users were never made aware that it was a study and the researcher analyzed the user's behaviors for weeks	NaN	Somewhat unacceptable	it is good that the researchers only examined data that was collected during the experiment period but unacceptable that the users were messaged privately and were not made aware that it was a experiment	NaN	Somewhat acceptable	it is good that users were made aware that it was a study and what the users had to do was related to the research topic	NaN	Neutral	it is good that the researchers only analyzed the users' behaviors for a short period after the experiment but it is not appropriate that the researchers never told the users that it was an experiment	NaN	Slightly important	Not at all important	Not at all important	Extremely important	Not at all important	Moderately important	Very important	Very important	Moderately important	NaN	6.0	2.0	7.0	3.0	4.0	5.0	1.0	long-term effects of the experiment	4.0	NaN	NaN	NaN	NaN	1	2	8
498	499	Twitter	55.0	Male	White / Caucasian	Vocational training	Very liberal	Moderately aware	[… are large and can contain millions of data points, … reflect events in real-time and can be collected continuously over time, … are naturalistic in that they do not require researchers to directly interact with research volunteers, … often capture social relationships not found using traditional methods (e.g. surveys), … are readily accessible to researchers and easy to collect]	[Privately messaging users, Publicly posting on users' profiles, Creating fake accounts ("bots")]	[Political elections (e.g. voting behavior), Presidential approval ratings, Communication (e.g. spread of opinions and hate-speech), Public sentiment (e.g. environment-related concerns), News consumption (e.g. sharing of misinformation), Social networks]	I think ethical approval is that an academic experiment is run in an ethical way. Meaning that the researchers adhere to ethical standards.	Somewhat unacceptable	the fact that participants were not aware they were part of a research study is a concern	NaN	Somewhat unacceptable	i find it somewhat unacceptable that researchers sent unsolicited private messages	if the researchers had contacted the twitter users instead of sending unsolicited private messages i would probably find it a little more ethical	NaN	NaN	NaN	Somewhat unacceptable	the users were not informed that they are part of an academic research and deceived by human looking automatic accounts	NaN	Moderately important	Not at all important	Moderately important	Moderately important	Moderately important	Very important	Extremely important	Slightly important	Moderately important	NaN	7.0	1.0	3.0	2.0	5.0	6.0	4.0	NaN	NaN	NaN	NaN	NaN	NaN	3	3	6

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